mcc kosovo labor force and time use study research report · 2020. 1. 23. · mcc kosovo lftus 2017...

February 2018 This report was prepared independently by Hira Siddiqui, Ardiana Gashi, Julia Higgins, Basab Dasgupta, and Mateusz Pucilowski of Social Impact, Inc. at the request of MCC.

Kosovo Labor Force and Time Use Study Research Report

Millennium Challenge Corporation

MCC Kosovo Labor Force and Time Use Study Research Report February 2018

The authors’ views expressed in this publication do not necessarily reflect the views of MCC.

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MCC Kosovo LFTUS 2017 MCC Kosovo LFTUS 2017

CONTENTS

52T52T1. Executive Summary52T52T .......................................................................................................... 8

52T52T 52T52T 52T52TIntroduction52T52T ..................................................................................................................... 25

52T52T 52T52T 52T52TReport Objectives 52T52T ........................................................................................................... 27

52T52T 52T52T 52T52TLiterature review 52T52T ............................................................................................................. 28

52T52T 52T52T 52T52TResearch Design 52T52T ............................................................................................................. 32

52T52TResearch Questions & Key Variables 52T52T ............................................................................................... 32

52T52TResearch Design Overview52T52T .............................................................................................................. 33

52T52TMeasurement Approach52T52T ................................................................................................................... 35 52T52TThe Eurostat Method52T52T .................................................................................................................... 35 52T52TEurostat Harmonized European Time Use Surveys 52T52T ...................................................................... 35 52T52TGlobal Entrepreneurship Monitor 52T52T .................................................................................................. 36 52T52TCompendium of Gender Scales: Gender Norm Attitudes Scale52T52T .................................................... 36

52T52TStudy Sample 52T52T ................................................................................................................................... 36 52T52TSampling Unit52T52T ............................................................................................................................... 36 52T52TSampling Strategy52T52T ........................................................................................................................ 36 52T52TSampling Frame and Selection 52T52T ..................................................................................................... 37 52T52TSample Size52T52T ................................................................................................................................. 38

52T52TPrimary Data Collection Details 52T52T ........................................................................................................ 38 52T52TInstruments52T52T ................................................................................................................................... 38 52T52TRespondents52T52T ................................................................................................................................ 38 52T52TSurvey Team52T52T ................................................................................................................................ 39 52T52TData Quality Assurance52T52T ................................................................................................................ 39

52T52T 52T52T 52T52TFindings 52T52T ........................................................................................................................... 42

52T52TDemographics52T52T .................................................................................................................................. 42

52T52TThe Eurostat Method 52T52T ........................................................................................................................ 45

52T52TEmployment52T52T ..................................................................................................................................... 46 52T52TEmployment rate52T52T .......................................................................................................................... 47 52T52TOccupation and Economic Activity52T52T ................................................................................................ 51 52T52TTypes of employment (contractual/non-contractual, government/private) 52T52T ..................................... 54 52T52TNet monthly salary52T52T ........................................................................................................................ 56 52T52TEmployment duration52T52T .................................................................................................................... 59 52T52TTime spent working 52T52T ...................................................................................................................... 59 52T52TPermanent versus Temporary Employment (temporary/permanent) 52T52T ............................................. 60 52T52TLevel of underemployment (full-time/part-time)52T52T ............................................................................. 63 52T52TLevel of vulnerable employment (self-employed with no employees or paid family workers 52T52T .......... 63 52T52TSeasonality in Employment52T52T ........................................................................................................... 64 52T52TActual versus Self-Declared Labor Status52T52T ..................................................................................... 67

52T52TUnemployment 52T52T ................................................................................................................................. 68 52T52TUnemployment rate52T52T ...................................................................................................................... 68 52T52TReasons for unemployment 52T52T .......................................................................................................... 71 52T52TOccupation of the unemployed 52T52T ..................................................................................................... 71

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52T52TTime spent searching for employment 52T52T .......................................................................................... 72 52T52TType and sector of work unemployed are searching for 52T52T ................................................................ 73

52T52TLabor Force Participation 52T52T ................................................................................................................. 74 52T52TLabor force participation rate52T52T ........................................................................................................ 75 52T52TReasons for inactivity52T52T .................................................................................................................... 77 52T52TDiscouraged workers52T52T .................................................................................................................... 79

52T52TEntrepreneurship52T52T .............................................................................................................................. 80 52T52TLevel of entrepreneurship 52T52T ............................................................................................................. 81 52T52TOpportunity vs. necessity driven entrepreneurship 52T52T ....................................................................... 83 52T52TShare of registered businesses52T52T .................................................................................................... 84 52T52TSources of start-up capital52T52T ............................................................................................................ 85 52T52TMain obstacles in growing businesses52T52T .......................................................................................... 87 52T52TWhether or not entrepreneurship is considered a desirable career choice 52T52T .................................... 88

52T52THousehold Income52T52T ........................................................................................................................... 89 52T52TForeign remittances as share of total income 52T52T ............................................................................... 91 52T52TForeign Remittance Use52T52T ............................................................................................................... 91 52T52TRemittances and Labor Market Outcomes52T52T .................................................................................... 92 52T52TAbility to work abroad and Labor Market Outcomes 52T52T ...................................................................... 93

52T52TTime Use52T52T .......................................................................................................................................... 94 52T52TTypes of Activities 52T52T ......................................................................................................................... 95 52T52TType of People Time Spent with 52T52T ................................................................................................. 102

52T52TGender Norms in Work and Society 52T52T ............................................................................................... 103 52T52TSocietal Gender Norms52T52T .............................................................................................................. 104 52T52TWorkplace Discrimination 52T52T ........................................................................................................... 106 52T52THousehold decision making 52T52T ........................................................................................................ 108 52T52TAsset Ownership52T52T ........................................................................................................................ 110

52T52T 52T52T 52T52TComparison of findings to other sources 52T52T ................................................................... 112

52T52TComparison to Existing Kosovo Data 52T52T ............................................................................................. 112

52T52TComparison to Similar European Economies52T52T ................................................................................. 113

52T52T 52T52T 52T52TConclusions52T52T .................................................................................................................. 116

52T52TConclusions 52T52T ................................................................................................................................... 116

52T52TLessons learned52T52T ............................................................................................................................. 117

52T52T 52T52T 52T52TAdministrative 52T52T ............................................................................................................... 119

52T52TInstitutional Review Board requirements and clearances 52T52T ............................................................... 119

52T52TData Protection, Access and Documentation plan 52T52T .......................................................................... 119 52T52TData Protection52T52T ........................................................................................................................... 119 52T52TData Access and Documentation52T52T ................................................................................................ 120

52T52TDissemination Plan52T52T ........................................................................................................................ 120

52T52TResearch Team Roles and Responsibilities 52T52T ................................................................................... 120

52T52T 52T52T 52T52TReferences52T52T ..................................................................................................................... 122

52T52T 52T52T 52T52TAnnexes 52T52T ......................................................................................................................... 124

52T52TAnnex 1: Data Collection Instruments 52T52T ............................................................................................ 124

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52T52TPre-screen and Household Roster52T52T .............................................................................................. 124 52T52TExtended Interview52T52T ..................................................................................................................... 151

52T52TAnnex 2: Weighting Plan 52T52T ................................................................................................................ 177

52T52TAnnex 3: Regression Analysis 52T52T ........................................................................................................ 178

52T52TAnnex 4: Disaggregated Indicators52T52T ................................................................................................. 180 52T52TEmployment status52T52T ..................................................................................................................... 180 52T52TEntrepreneurship52T52T ........................................................................................................................ 189 52T52TTime Use52T52T .................................................................................................................................... 196

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TABLES & FIGURES

TABLE OF TABLES

52TU52TUTable 1: Research Outcomes of Interest UU52T52T ...................................................................................32 52TU52TUTable 2: Breakdown of Survey Instruments UU52T52T ...............................................................................34 52TU52TUTable 3:Distribution of surveys completed across EAs by region UU52T52T ..............................................42 52TU52TUTable 4: Distribution of urban and rural EAs by region UU52T52T ..............................................................43 52TU52TUTable 5: Highest Level of Education, by SexUU52T52T .............................................................................44 52TU52TUTable 6: Field of Study, by SexUU52T52T .................................................................................................44 52TU52TUTable 7: Definitions of Eurostat Terms UU52T52T ......................................................................................45 52TU52TUTable 8: Employment Rate (%), by Region and Geographic Location UU52T52T .......................................48 52TU52TUTable 9: Employment Rate (%), by Highest Level of Education and Sex UU52T52T ...................................49 52TU52TUTable 10: Employment Rate (%), by Field of Study UU52T52T ...................................................................49 52TU52TUTable 11: Occupation (%), by Sex and Geographic LocationUU52T52T ....................................................51 52TU52TUTable 12: Distribution of Occupations (%), by Region UU52T52T ...............................................................52 52TU52TUTable 13 Distribution of jobs in different sectors (%), by Sex UU52T52T .....................................................53 52TU52TUTable 14: Type of Employment (%), by Geographic Location UU52T52T ....................................................55 52TU52TUTable 15: Percentage of Contractual Jobs, by Employer Type UU52T52T ..................................................55 52TU52TUTable 16: Type of Employer (%), by Sex and Age Group UU52T52T ..........................................................56 52TU52TUTable 17: Average Monthly Salary, by Sex and RegionUU52T52T .............................................................56 52TU52TUTable 18: Average Monthly Salary (n), by Job Type UU52T52T ..................................................................57 52TU52TUTable 19: Average Monthly Income, by Job Sector UU52T52T ...................................................................57 52TU52TUTable 20: Average Monthly Salary, by Level of Education and SexUU52T52T ...........................................59 52TU52TUTable 21: Level of Permanent Employment (%), by Region UU52T52T ......................................................61 52TU52TUTable 22: Level of Permanent Employment (%), by Sector UU52T52T .......................................................61 52TU52TUTable 23: Reasons for Temporary Employment (%) UU52T52T ..................................................................62 52TU52TUTable 24: Reasons for Part-time Work (%)UU52T52T ................................................................................63 52TU52TUTable 25: Vulnerable Employment (%), by Geographic Location and SexUU52T52T .................................64 52TU52TUTable 26: Employment by Sector and SeasonUU52T52T ..........................................................................66 52TU52TUTable 27: Actual versus Self-Declared Labor Status UU52T52T .................................................................67 52TU52TUTable 28: Unemployment Rate (%), by Geographic Location and Sex UU52T52T ......................................69 52TU52TUTable 29: Unemployment Rate (%), by RegionUU52T52T .........................................................................69 52TU52TUTable 30: Unemployment Rate (%), by Highest Level of Education and Sex UU52T52T .............................69 52TU52TUTable 31: Unemployment Rate (%), by Field of Study UU52T52T ...............................................................70 52TU52TUTable 37: Labor Force Participation Rate (%), by Geographic Classification and SexUU52T52T ...............76 52TU52TUTable 38: Labor Force Participation Rate (%), by Highest Level of Education UU52T52T ...........................76 52TU52TUTable 39: Labor Force Participation Rate (%), by Field of Study UU52T52T ...............................................77 52TU52TUTable 40: Reasons for Inactivity (%), by SexUU52T52T .............................................................................78 52TU52TUTable 41: Reason for Inactivity (%), by Age UU52T52T ..............................................................................78 52TU52TUTable 42: Discouraged Workers among working age population (%), by Highest Level of EducationUU52T52T ..................................................................................................................................79 52TU52TUTable 43: Discouraged Workers (%), by Field of StudyUU52T52T .............................................................80 52TU52TUTable 44: GEM Stages of EntrepreneurshipUU52T52T ..............................................................................81 52TU52TUTable 45: Entrepreneurship Indicators (%), by Sex UU52T52T ...................................................................81 52TU52TUTable 46: Type of Entrepreneur (%), by Sex UU52T52T .............................................................................82 52TU52TUTable 47: Business Sectors (%), by SexUU52T52T ...................................................................................83 52TU52TUTable 48: Opportunity versus Necessity-driven Entrepreneurship (%), by RegionUU52T52T .....................84

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52TU52TUTable 49: Share of Registered Businesses (%) and Total No. of Businesses (n), by Business SectorUU52T52T .......................................................................................................................................85 52TU52TUTable 50: Main Sources of Start-up Capital (%), by SexUU52T52T ............................................................86 52TU52TUTable 51: Main Sources of Start-up Capital (%), by Geographic LocationUU52T52T .................................86 52TU52TUTable 52: Main Sources of Start-up Capital (%), by Region UU52T52T ......................................................87 52TU52TUTable 53: Main Obstacles to Business Growth (%, n) UU52T52T................................................................87 52TU52TUTable 54: Perceptions of Entrepreneurship (%), by Region UU52T52T .......................................................89 52TU52TUTable 55: Reasons for Negative Perceptions of Entrepreneurship (%, n) UU52T52T ..................................89 52TU52TUTable 56: Annual Household Income (%), by Region UU52T52T ................................................................90 52TU52TUTable 57: Remittance Incidence (%) and Share of Remittances (%), by Region UU52T52T .......................91 52TU52TUTable 58: Remittance Usage (Purpose) by Geographic Location UU52T52T ..............................................92 52TU52TUTable 59: Employment Rate (%) and Labor Force Participation Rate (%), by Remittance Usage UU52T52T

.................................................................................................................................................92 52TU52TUTable 60: Labor Market Outcomes (%), by ability to work abroad UU52T52T..............................................93 52TU52TUTable 61: Average Time Spent on Each Activity (hours), by Sex, Geographic Location, Employment Status, and Labor Force Status UU52T52T ............................................................................95 52TU52TUTable 62: Average Time Spent with Each Type of Person (Hours) UU52T52T .......................................... 103 52TU52TUTable 63: Agreement with Gender Norm Statements (%), by Sex and Geographic Location UU52T52T .. 104 52TU52TUTable 64: Whether Concern About Discrimination Has Influenced Career Decisions, (%), by Sex UU52T52T

............................................................................................................................................... 107 52TU52TUTable 65: Whether Access to Child Care has Influenced Career Decisions, (%), by Sex UU52T52T ........ 108 52TU52TUTable 66: Whether Lack of Access to Transportation has Influenced Career Decisions (%), by Geographic LocationUU52T52T ............................................................................................................... 108 52TU52TUTable 67: Decision-Making Power for Household Expenditures (%), by Sex UU52T52T .......................... 109 52TU52TUTable 68: Household Members with Decision-Making Power (%), by SexUU52T52T ............................... 109 52TU52TUTable 69: Individuals Indicating “Self” as Owning Each Type of Asset Either Solely or Jointly with Others (%), by SexUU52T52T .................................................................................................................. 110 52TU52TUTable 70: Comparison of LFTUS Data to Existing LFS Data UU52T52T ................................................... 112 52TU52TUTable 71: Comparison of LFTUS Data to LFS Data from Other Western Balkan Countries UU52T52T ..... 114 52TU52TUTable 72: Roles and Responsibilities of LFTUS Research Team Members UU52T52T ............................. 121

TABLE OF FIGURES

Figure 1: Key Labor Market Outcomes 10 Figure 2: Unemployment Rate, by Region 12 Figure 3: Unemployment Rate by Region and Geographic Classification 13 Figure 4: Employment and Unemployment Rate, by Field of Study 14 Figure 5: Proportion Working in a Job Related to their Field of Study, by Field of Study 15 Figure 6: Inactivity Reasons, by Sex 15 Figure 7: Average Hours Spent on Each Activity on a Typical Day 16 Figure 8: Proportion of Males Performing Each Activity Through the Day 17 Figure 9: Proportion of Females Performing Each Activity Through the Day 17 Figure 10: Proportion of Employed Individuals Performing Each Activity Through the Day 18 Figure 11: Proportion of Unemployed Individuals Performing Each Activity Through the Day 18 Figure 12: Labor Force Participation Rate by Level of Remittances Received 19 Figure 13: Level of Entrepreneurship, by Sex 21 Figure 14: Proportion of Businesses Owned by Males and Females, by Sector 22 Figure 15: Sources of Start-up Capital 22 Figure 16: Eurostat Methodology (Diagram) 46

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Figure 17: Employment Rate (%), by Age and Sex 48 Figure 18: Proportion Working in a Job Related to their Field of Study, by Field of Study 50 Figure 19: Type of Employment (%), by Sex 54 Figure 20: Hours Worked per Week, by Sex 60 Figure 21: Level of Permanent Employment (%), by Occupation 61 Figure 22: Vulnerable Employment (%), by Employment Sector 64 Figure 23: Employment Rate (%), by Sex and Season 65 Figure 24: Hours Spent Working per Week, by Sex and Season 67 Figure 25: Unemployment Rate (%), by Age and Sex 68 Figure 26: Time Spent Searching for Employment (years), by Sex 73 Figure 27: Labor Force Participation Rate (%), by Age and Sex 75 Figure 28: Labor Force Participation Rate by Level of Remittances Received 93 Figure 29: Proportion of Individuals Performing Each Activity (Aggregate) 98 Figure 30: Proportion of Males Performing Each Activity 99 Figure 31: Proportion of Females Performing Each Activity 99 Figure 32: Proportion of Urban Residents Performing Each Activity 100 Figure 33: Proportion of Rural Residents Performing Each Activity 100 Figure 34: Proportion of Employed Individuals Performing Each Activity 101 Figure 35: Proportion of Unemployed Individuals Performing Each Activity 101 Figure 36: Proportion of Active Individuals Performing Each Activity 102 Figure 37: Proportion of Inactive Individuals Performing Each Activity 102 Figure 38: Gender Norm Attitudes Index, by Education Level 106 Figure 39: Who is More Focused on Their Career, (%), by Sex 107

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ACRONYMS

Acronym Definition CAPI Computer-Assisted Personal Interviewing CITI Collaborative Institutional Training Initiative COR Contracting Officer’s Representative DCMI Dublin Core Metadata Initiative DDI Data Documentation Initiative DRB Disclosure Review Board EA Enumeration Area EI Extended Interview EU European Union FHI 360 Family Health International 360 GDP Gross Domestic Product GEM Global Entrepreneurship Monitor GNAS Gender Norm Attitudes Scale HQ Headquarters IDRA IDRA Research and Consulting (Data Collection Firm) ILO International Labor Organization ILOSTAT International Labor Organization Statistics Database IRB Institutional Review Board ISCO Standard Classification of Occupations (ILO) KAS Kosovo Agency of Statistics KBRA Kosovo Business Registration Agency KCD Kosovo Center of Diplomacy LFS Labor Force Survey LFTUS Labor Force and Time Use Study MCA-M Millennium Challenge Account MCC Millennium Challenge Corporation NACE National Association for Continuing Education (Education categories) NGO Non-governmental Organization NIH National Institutes of Health ODK Open Data Kit OECD Organization for Economic Cooperation and Development PII Personal Identifying Information PM Project Manager PSU Primary Sampling Unit QA Quality Assurance RFQ Request for Quotation RT Research Team SI Social Impact TV Television UN United Nations USAID United States Agency for International Development USD United States Dollar

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1. EXECUTIVE SUMMARY

Background

The MCC business model relies on sound economic data as an input to program design. While

there have been a number of large-scale surveys measuring employment behavior in Kosovo,

extant data sources either lack the scope to inform concrete programmatic decisions, or suffer

from questionable validity. Seeking to fill this empirical gap, MCC contracted Social Impact (SI) to

implement a nationally and regionally representative survey to shed light on Kosovo’s

macroeconomic indicators.

Research Questions & Methodology

The Labor Force and Time Use Survey (LFTUS) was designed to answer nine research questions

concerning population-based estimates of the following: labor force participation, job seeking

behavior, time use, household decision-making, workplace discrimination, and prevalence and

inhibiting factors of entrepreneurship. Given the centrality of labor force participation, the research

was designed to examine the relationships between this indicator and field of study, gender

norms, and remittances.

To answer these questions, the research team administered a nationally and regionally

representative household survey with 8,533 households across 854 Enumeration Areas (EAs).

The study used multi-stage stratified cluster-randomized sampling to arrive at a representative

sample for each of Kosovo’s seven regions. To calculate the required sample size, the research

team used the 2015 Kosovo Agency of Statistics (KAS) Labor Force Survey to determine

prevalence rates for key indicators of interest and calculated intra-cluster correlations on key

variables to inform design effects. The sampling frame for household selection was created

through an in-person listing exercise that physically mapped all households in sampled EAs. To

ground the research design in the most recent literature and promote comparability with other

datasets, the study’s instrumentation and methodological approach was heavily informed by four

core resources: the Eurostat approach for measurement of labor force participation, the Eurostat

guidelines on harmonized European time use surveys, the Global Entrepreneurship Monitor, and

C-Change/USAID’s Gender Norm Attitudes Scale.

For each sampled household, enumerators attempted to 1) populate a Household Roster with the

head of household or the member most knowledgeable about the labor force status of all

household members and 2) complete an Extended Interview (EI) with one randomly selected

female and one randomly selected male, who were non-students aged 18-74 years. The

Household Roster captured the labor force status of all members of the household above the age

of 15, while the Extended Interview captured detailed information on access to productive capital,

household decision-making, time use, and gender attitudes. Data collection was implemented

through SI’s local partner, IDRA Research and Consulting, and occurred over a 17-week period

between March 31, 2017 and August 4, 2017.

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The research team enumerated 8,533 households, collecting employment information on 32,742

individuals and completing 8,604 extended interviews. The roster sample was evenly balanced in

its gender distribution (50.2% female and 49.8% male), though the extended interviews were

slightly skewed toward female respondents (56.9% female and 43.1% male). The ethnic

breakdown of the sample reflected the most recent census, with 91.6% of respondents identifying

as Albanian. Males were, on average, better educated, with two thirds of male respondents having

finished upper secondary school or higher. The comparable rate for females was 40%.

Findings

Question 1: What is the true level of employment (formal and informal),

underemployment or vulnerable employment, and unemployment, disaggregated

by gender and district? How much time have the un- or under-employed spent

looking for a job? Are they an entrepreneur?

Approximately 72% of Kosovo’s population is of working age, following the Eurostat definition of

15-74 years.P0FP0FP0FP0FP0FP0FP0 FP0F

1PPPPPPPP Given the Kosovar retirement age is 65, it is important to note the LFTUS treats

retired individuals aged 65-74 years as unemployed if they were actively seeking a job or inactive

if they were not actively seeking a job. Figure 1 below displays the key labor market indicators for

Kosovo.

1 KAS defines working age as 15-64.

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Figure 1: Key Labor Market Outcomes

Labor Force Participation

The labor force participation rate, or the proportion of working age individuals that are

economically active, was 49.6%. Mirroring broader economic trends, males were more than twice

as likely to be in the labor force than females (67.9% and 31.3%, respectively). Among the 50.4%

of the economically inactive population, 85.4% reported never having been regularly employed

before.

Employment

The national employment rate, also known as the employment to population ratio, was 41.1%.

Said differently, only two out of five working-age individuals were employed at the time of the

survey. Employment trends evidenced a stark gender divide, with males employed at 2.4 times

the rate of females (58.3% and 24.1%, respectively). Youth unemployment was especially low,

with only one in four persons aged 15-24 employed at the time of the survey.

Persons aged 14 and below

25%

Working Age Population (15-74)

72%

Active

49.6%

67.9% 31.3%

Employed

83.7 %

86.4% 77.6%

Employee

60.8%

Part-time

10.5%

Full-time

89.5%

Self-employed

16.2%

Unpaid Family Worker

23.0%

Unemployed

16.3%

13.6% 22.4%

Short-term

66.4%

Long-term

33.6%

Inactive

50.4%

32.1% 68.7%

Never Worked

85.4%

Worked Before

14.6%

Persons aged 75 and above

3%

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Among the employed, 60.8% were employees, 23% reported doing unpaid family work, and

16.2% were self-employed. One in four self-identified employees worked for the public sector.

One in three employed individuals was found to be in a state of vulnerable employment, defined

as being either an unpaid family worker or self-employed without employees. This situation was

fifty percent more common among females than males (46.0% and 30.7%, respectively). Of those

who were in vulnerable employment, the highest proportion, 57.9%, worked in the agriculture

sector. Given this finding, it is no surprise that vulnerable employment in rural areas was 2.25

times that of urban areas (39.5% and 17.5%, respectively).

One in ten self-identified employees were part-time workers and one in two reported being in a

state of temporary employment. Almost all (93.9%) of the temporarily employed reported their

contracts as subject to regular renewal. This suggests that these jobs, while temporary in their

technical classification, do in fact provide stable employment. There was a pronounced gender

difference in formality of employment (contractual versus non-contractual), with females were

30% more likely to report working under a contract than males (71.9% and 55.5%, respectively).

Government and state-owned enterprises almost uniformly provided formal employment (>95%),

while only 44% private companies did so.

The average monthly salary of employed individuals, excluding unpaid workers, was €340. Males

earned an additional €64, almost twenty percent more, than females. Job income varied by type

of employer, with employees of international organizations receiving the highest average salary

(€789), followed by those working for state-owned enterprises (€442), and the government (€415).

The lowest average salary was reported by respondents who worked for other private individuals

(€198).

Unemployment

The national unemployment rate was 16.3%. As with employment, there was a notable difference

between male and female respondents. Females were 65% more likely to be unemployed than

males (22.4% and 13.6%, respectively). Youth unemployment was pronounced, with 29.2% of

persons aged 15-24 years jobless at the time of the survey. The gender differences were even

more pronounced among this sub-population, with female youth almost twice as likely to be

unemployed as male youth.

Disaggregating by region, Pristina had the highest unemployment rate (24.3%), while Gjakova

had the lowest (9.5%). All remaining four regions had employment rates closer to the national

average of 16.3%. Figure 2 shows unemployment rates by region. It may be surprising that

Pristina, which contains the nation’s capital, has the highest unemployment rate. This finding is

primarily driven by unusually high rates of rural unemployment in Pristina. Additionally, it is of note

that residents of Pristina also report the highest monthly salaries (€360) and have the lowest

levels of vulnerable employment (18.8%). Conversely, While Gjakova has the lowest

unemployment rate, average monthly salaries reported by its residents were the lowest in the

country (€298) and the rate of vulnerable employment was the highest (56.7%).

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Figure 2: Unemployment Rate, by Region

The unemployment rate was higher in urban areas for both male and female respondents.

Nationwide, 21% of economically active urban dwellers were unemployed. The comparable figure

for rural areas was 15%. All regions except for Pristina had higher urban unemployment rates

compared to rural areas, with Gjakova and Mitrovica having the highest urban-rural disparity.

Figure 3 shows unemployment rates by region and geographic classification.

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Figure 3: Unemployment Rate by Region and Geographic Classification

The average time unemployed individuals spent looking for a job was 18.8 months (over 1.5

years). According to Eurostat, individuals are considered to be in long-term unemployment if they

have been searching for a job for more than one year. Following this definition, one-third of

unemployed individuals were in a state of long-term unemployment.

Question 2: Is there a relationship between field of study and having employment?

How many people are employed in the field that they studied? Are there any

correlations between certain fields of study or location and unemployment?

The most popular fields of study for those having reached at least the upper secondary level were

Social Sciences (18.8%), Crafts and Trades (13.3%), and Engineering (12.4%). While Social

Sciences were popular with both sexes, males were overly represented in the Crafts/Trades and

Engineering fields, while a higher proportion of females opted to pursue schooling in Social

Sciences and Health/Welfare.

Employment varied significantly across fields of study. Figure 4 shows the popularity as well as

employment and unemployment rates for each field of study. Respondents with studies focusing

on Services had the highest employment rates (66.1%), followed closely by Engineering (64.7%)

and Teacher Training/Education Science (61.6%). The most popular field of study, where females

predominate, Social Sciences, had the fifth lowest employment rate (49.6%) among all fields,

while the second most popular field of study, Engineering, had the second highest employment

rate (64.7%).

Similarly, unemployment also varied across fields of study, with the highest rates recorded among

individuals who studied Business (31.1%), Manufacturing and Construction (28.8%), and Life

Science (25.9%). The lowest rates were found among those who studied Services (12%), General

Programs (12.3%), Engineering (13.2%) and Crafts/Trades (15.5%).

8.5

12.613.8 13.4 13.2

15.9

25.8

17.6

22.2

16.5

20.621.9

20.721.9

Gjakova Prizren Ferizaj Peja Mitrovica Gjilan Pristina

Rural Urban

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Figure 4: Employment and Unemployment Rate, by Field of Study

Only one in three individuals reported their current or most recent job to be related to their primary

field of study. Figure 5 demonstrates the relationship between popularity of each field of study

and employment rates in the field of study. The lowest relatability of jobs to field of study was

found to be in General Programs (8%), followed by Physical Science (15%), and Mathematics

and Statistics (27%). The highest relatability was found to be in Teacher Training and Education

(76%), Health and Welfare (62%) and Life Science (51%).

0%

10%

20%

30%

40%

50%

60%

70%

Proportion of Individuals in Field of Study Employment Rate Unemployment Rate

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Figure 5: Proportion Working in a Job Related to their Field of Study, by Field of Study

The 66.2% of individuals who indicated that their current or most recent job was not related to

their field of study were asked about why that was the case. An overwhelming majority of them,

91.9%, indicated that it was because there were no jobs available in their field of study.

Question 3: For people not looking for jobs, why are they not looking?

Economically inactive individuals, who make up 50.4% of the working age population, were asked

about why they are not looking for jobs. Figure 6 shows the most commonly cited reasons for

male and female respondents.

Figure 6: Inactivity Reasons, by Sex

0%

10%

20%

30%

40%

50%

60%

70%

80%

Proportion of Individuals in Field of Study Job Related to Field of Study

26.4%36.6%

21.7%

22.2% 1.1% 32.2%

18.7%

24.6%

16.0%

13.1% 19.8% 10.0%

7.8%10.8%

6.4%

Total Male Female

Other

Not Qualified To Work

Own Illness Or Disability

Retired

Believes That No Work IsAvailable

Looking After Children

Undergoing School, Education,Or Training

SOCIALIMPACT.COM 16


The most common reasons for economic inactivity were participation in professional education or

training (26.4%), looking after children (22.2%), belief that no work is available (18.7%), and

retirement (13.1%). Reasons for inactivity varied greatly by gender. Males were much more likely

to cite education or training, belief that no work is available, or retirement as their reason for

inactivity while the leading reason for females was due to their childcare duties (30.2%). The

comparable figure for males was only 1.1%.

Survey results indicate that 18.7% of the inactive population were discouraged workers, i.e. those

who have stopped looking for work because they believe that no work is available. The rates were

slightly higher for males than females (24.6% and 16%, respectively).

Question 4: What are people doing with their time? Especially, what are women or

the unemployed doing with their time? How much time is spent on child care and

elder care? How much time is spent commuting?

Extended Interview respondents were asked to record time-use over the 24-hour period preceding

the interview. In decreasing order, the most common activities were sleeping and performing

personal care activities (10.8 hours per day), household and family care (5.1 hours), and social

life and entertainment (3.2 hours). The average Kosovar spent only 2 hours on employment on a

given day, which is not surprising given that half of the population is economically inactive. Among

the employed, the average Kosovar spends 4.8 hours a day working. Figure 7 show average time-

use for Kosovars aged 18-74, on a typical day.

Figure 7: Average Hours Spent on Each Activity on a Typical Day

The data show substantial variation in time use by sex. Males spend, on average, 400% more

time on employment than females (3.9 hours and 0.9 hours, respectively). In contrast, women

spend 300% more time on household and family care than men (7.1 hours and 2.3 hours,

respectively). Males also spend more time on travel compared to females, which largely comes

from travel related to work. Use of mass media peaks for both males and females at 9 PM. Figures

Sleeping And Personal Care

10.8

Household And Family Care

5.2

Social Life And Entertainment

3.2

Employment2.0

Mass Media1.5 Travel

0.5

Other0.7

SOCIALIMPACT.COM 17


8 and 9 show the proportion of males and females performing each activity through a 24-hour

time period starting at 4 AM.

Figure 8: Proportion of Males Performing Each Activity Through the Day

Figure 9: Proportion of Females Performing Each Activity Through the Day

Looking at time use by employment status, employed individuals spend on average 4.8 hours on

employment in a day, while unemployed individuals spend 0.3 hours searching for jobs (coded

as employment in Figure 11). There were no differences in the time spent on travel by employment

status. Figures 10 and 11 highlight the difference in time use between employed and unemployed

individuals.


Employment



Mass Media

Travel

4 AM 6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM 12 AM 2 AM


Employment



Mass MediaTravel


SOCIALIMPACT.COM 18


Figure 10: Proportion of Employed Individuals Performing Each Activity Through the Day

Figure 11: Proportion of Unemployed Individuals Performing Each Activity Through the Day

Question 5: How does receiving remittances and/or the ability to work abroad

impact labor market decisions?

One in five households received remittances from outside of Kosovo. Among recipients, the

average household received 47.7% of its income from remittances. For a quarter of the remittance

recipients, remittances make up 80-100 percent of their total household income. The labor force

participation rate for individuals belonging to remittance receiving households was 2.5 percentage

points lower compared to individuals belonging to non-remittance receiving households, for whom

the labor force participation rate stood at 51%. This estimate was statistically significant. This

trend is more pronounced when looking at labor force participation rate by the level of remittances

received. Figure 12 shows the participation rates for individuals belonging to households in each

category and the respective differences in their participation rates compared to those of

individuals belonging to non-remittance receiving households.


Employment



Mass Media

Travel



Employment



Mass Media

Travel


SOCIALIMPACT.COM 19


Figure 12: Labor Force Participation Rate by Level of Remittances Received

While no significant differences were found for any of the middle levels of remittances, labor force

participation rates for those receiving remittances between 80-100 percent of their household

income were 9.6 percentage points lower (this difference was statistically significant) compared

to individuals belonging to non-remittance receiving households. This indicates that remittances

influence individuals’ labor market outcomes, particularly at very high level of remittances.

About 15.9% of Extended Interview respondents were asked whether they plan to work abroad in

the next 12 months. Employment rates were 5.3 percentage points higher and activity rates were

18.6 percentage points higher for those who planned to work abroad compared to those who

didn’t. This could either indicate that those who intend to work abroad or have the ability to do so

are also more likely to be active/employed. However, a more likely explanation is that only

individuals who are already active/employed would plan to work in another country. These

findings indicate that there is a correlation between employment status and intent to work abroad,

but don’t shed light on causation. Interestingly, unemployment rates for those who intend to work

abroad are 2.4 times higher compared to those who do not. This could indicate those who have

not been able to find employment in Kosovo are more likely to want to look for employment

abroad.

Question 6: How do attitudes about women's place in family and society influence

women's participation in the labor market?

Using an index constructed from responses to the Gender Norm Attitudes Scale (GNAS), female

respondents were found to hold less equitable (i.e. more traditional) gender norms than males.

Less equitable gender norms were more common in rural areas and amongst less educated

respondents. The research team explored correlations between gender norms of members of the

household and female participation rates, but did not find anything of significance. While females

are underrepresented in the Kosovar workforce, more likely to be unemployed, and, when

employed, more likely to be unpaid family workers or in a situation of vulnerable employment,

survey data do not support the notion that unequitable labor force outcomes are solely the result

of overtly communicated gender norms.

51.0% 52.0% 52.1% 47.2% 51.5% 48.6% 41.5%

1.0% 1.1% -3.9% 0.5% -2.4% -9.6%

NoRemittances

< 10 10-20 20-40 40-60 60-80 80-100

*

SOCIALIMPACT.COM 20


Question 7: How is women’s engagement in productive activities (like starting a

business) related to ownership and ability to control assets/access to credit?

Self-reported household asset ownership was substantially more pronounced among male

respondents. Females were statistically less likely to report ownership across all fifteen asset

categories, including land, livestock, and durable goods. Female entrepreneurs had higher

ownership rates than non-entrepreneurial females (9 percentage point difference on an asset

index), indicating either that these females have more ownership because they are enterprising

enough to start a business and can negotiate more control over the households’ assets, or

conversely that having higher asset ownership provides females with the resources to start a

business. It should also be noted that while asset ownership of entrepreneurial females was high,

the average female entrepreneur still had less control over household assets than the average

male. With regard to credit, female entrepreneurs were 50% more likely to report relying on formal

loans as the primary source of business start-up capital than male entrepreneurs. When

compared to the finding that male entrepreneurs are more likely to rely on their own resources or

remittances in starting businesses, it is clear the female entrepreneurs are disproportionately

burdened by interest-burdened formal loans.

Question 8: Have discriminatory practices in hiring or while employed, or

perceptions that there might be such practices, discouraged women from seeking

jobs?

Extended Interview respondents who were employed currently or in the past were asked whether

they were treated equally within the workplace by their employer(s) as compared to members of

the opposite sex that they work with. Almost all respondents (96.7%) reported being treated

equally, with no significant differences between males and females. This could indicate that either

that there is very little discrimination in Kosovo, that negative gender norms are so internalized

that individuals don’t recognize discrimination when it happens, or there was systematic response

bias.

Of the 3.3% of individuals who were not treated equally, two out of three indicated that unequal

treatment had prevented them from advancing in their career. A higher proportion of males

indicated discrimination as a barrier to growth compared to females (67.4% and 51.4%,

respectively). This could indicate a higher level of internalization of discrimination by women such

that they don’t consider discrimination to be a factor. This is corroborated by the fact that when

asked about who is focused more on their career, while most individuals responded that that there

were no differences between the sexes (58.1%), three times more people reported men to be

more driven than women (31.4% and 10.5%, respectively).

In addition, when individuals were asked whether concern about discrimination had influenced

their career decisions, only 3.1% of individuals indicated it to be a concern, with no differences in

response by sex. It’s very difficult to capture the true level of discrimination, therefore the response

to this question should be taken with a grain of salt, especially given that females are more likely

to hold inequitable gender norms than males which might have led to underreporting of issues

related to discrimination. It should also be noted here that when individuals were asked about the

SOCIALIMPACT.COM 21


main reason for their unemployment, about 4.4% had indicated discrimination and/or unsafe work

environment as a reason; there no were differences between males and females.

Question 9: What are the factors that inhibit entrepreneurship, especially for

women vs. men, and lead to the low levels of entrepreneurial activity for men and/or

women?

The national level of entrepreneurship was 8.5%, with 6.1% of individuals indicating already

having started a business and 2.5% indicating being in the process of starting a business. Males

were four times more likely to report entrepreneurial behavior. Figure 13 shows the level of

entrepreneurship among survey respondents disaggregated by sex.

Figure 13: Level of Entrepreneurship, by Sex

The rate of nascent entrepreneurship (less than 4 months old and not paid any salaries or wages)

was 3.4%, new business ownership (between 4 and 42 months old paying salaries or wages) was

0.4% and established business ownership (paying salaries or wages for more than 42 months)

was 1.4%. One in three businesses were in the Agriculture, Forestry and Fishing sector. The other

most popular sectors were Wholesale and Retail Trade (18.4%), Service Activities (16.3%), and

manufacturing (10.4%). Figure 14 shows the proportion of male and female entrepreneurs in the

8 most popular business sectors which accounted for 95% of the businesses. All sectors were

dominated by males, especially the electricity, gas, steam, air conditioning supply, construction,

transportation and storage sectors. The highest proportion of female entrepreneurs were in

service activities, accommodation, and agriculture.

10.0%

4.2%

2.2%0.9%

6.1%

2.5%

Already Started a Business Starting a Business

Male Female Total

SOCIALIMPACT.COM 22


Figure 14: Proportion of Businesses Owned by Males and Females, by Sector

Unsurprisingly, the biggest obstacle to business growth was identified as lack of financial means

(65%). Other obstacles to growth included lack of market and clients (13.4%), high competition

(9.9%), and institutional barriers and lack of institutional support (3.4%). There were no gender

differences noted in the obstacles to business growth. However, there were some gender

differences in the sources of capital to start a business (Figure 15). The most common source of

start-up capital for both males and females was personal resources and savings, however,

females were more likely to use formal loans compared to males. It should also be noted that the

fact that a high number of individuals indicate lack of financial resources as the main obstacle to

business growth is corroborated by fact that 69% of business are started with personal resources,

savings and remittances, while only 23.6% are started with formal and informal loans.

Figure 15: Sources of Start-up Capital

To further explore inhibiting factors of entrepreneurship, the research team asked individuals

about whether or not entrepreneurship is considered to be a desirable choice in Kosovo. More

than 97% of individuals indicated that it was, with no gender differences. Respondents were also

asked whether or not they perceived there to be good opportunities for starting a business in their

area in the next month. Sixty percent of individuals responded in the negative, with no gender

difference. When individuals were asked whether they possessed the knowledge, skill and

experience required to start a new business, 72.6% of males indicated that they did, compared to

100.0

98.0

94.8

87.0

80.1

79.3

75.7

71.2

0.0

2.0

5.2

13.0

19.9

20.7

24.3

28.8

Electricity, Gas, Steam, Air Conditioning Supply

Construction

Transportation And Storage

Wholesale/Retail Trade; Repair Of Motor Vehicles

Manufacturing

Agriculture, Forestry And Fishing

Accommodation And Food Service Activities

Other Service Activities

Male Female

60.1%

61.1%

55.8%

20.4%

18.7%

27.7%

3.2%

3.2%

3.3%

9.1%

9.7%

6.3%

7.3%

7.3%

6.9%

Total

Male

Female

Own Resources/Savings Formal Loan Informal Loan Remittances Other

SOCIALIMPACT.COM 23


only 43.1% of females, which leads one to believe that females exhibit low levels of

entrepreneurship because they don’t feel confident about their ability to start and run a business.

Lessons Learned

Labor force indicators estimated in this study different substantially from previous estimates,

including findings reported by KAS. The primary driver of this divergence is likely the higher rates

of agricultural and unpaid family workers captured by this study. These individuals seem to have

been classified as economically inactive in past estimates, artificially dampening employment

rates. It should also be noted that past estimates are substantially more aligned with the colloquial,

rather than economic, definition of employment. This theory was empirically tested by asking

respondents about self-declared labor status. When the employment rate was calculated using

self-declared labor status, the estimate was 10.4 percentage points lower than when calculated

using the Eurostat methodology (30.8% and 41.1%, respectively). P1FP1FP1FP1FP1 FP1FP1FP1F

2PPPPPPPP This indicates that asking

directly about employment status results in a downward bias relative to the Eurostat method, due

primarily to unpaid family workers.

Recognizing this discrepancy, the research team employed four strategies to ensuring accurate

measurement of respondent employment status. First, the LTFUS survey was programmed to

ask the Eurostat question inquiring about work or absence from work in the reference week

(regular job, occasional job for self/family, occasional job for another enterprise, agricultural job)

as four different questions. This was done to ensure that enumerators were explicitly probing

respondents about each type of work. Second, question phrasing included culturally appropriate

examples of each type of work to improve respondents’ comprehension. Third, enumerators were

trained on how to emphasize and intonate key phrases when reading questions to differentiate

between the different types of work. Fourth, speed violations feature embedded in the research

team’s electronic data collection software was also used to track and remediate enumerators

through real-time data monitoring, issuing warnings whenever they were found to be going over

labor questions too quickly. This approach differs significantly from the KAS methodology, which

asks the Eurostat employment item in a single checkbox-style question.

Beyond the employment questions, the LFTUS research team made significant efforts to adhere

to the highest standards in data collection practice. The study employed various strategies for

promoting the integrity of survey data. One of the key aspects of the approach was paying

enumerators a monthly salary as opposed to remunerating on a per-survey basis. This is

important because the latter creates adverse incentives for enumerators, promoting volume and

not quality of work. Additionally, detailed tracking protocols were put in place to ensure that

enumerators were not replacing households prematurely and that a robust proportion of the

original randomized sample was reached. For surveys administered in Serb Kosovar areas, the

survey firm employed enumerators with the appropriate ethnicity and experience to avoid non-

response due to mistrust.

2 The self-declared estimate is only 2.8 percentage points higher than the 2016 KAS estimate (30.8% and 28%, respectively).

SOCIALIMPACT.COM 24


Before commencing fieldwork, rigorous training was provided - over a period of 5 days, followed

by 2 days of piloting - to enumerators and their supervisors on survey tools and protocols.

Enumerators were also handed physical copies of the enumerator manual during training that

included detailed instructions on how to administer each question and outlined good practices in

data collection. It is important to note that approximately 10 percent more trainees than required

were engaged in the training and only top performers were selected to participate in the study

based on their performance on the final quiz and during the pilot exercise. A substantial amount

of time on the first day of the training was spent informing enumerators about the importance of

the survey and explaining the Eurostat methodology. Training also emphasized ways of probing

respondents to obtain the most accurate data on employment.

Heavy enumerator oversight was ensured by experienced supervisors, such that there was one

supervisor for every 8 enumerators, with supplementary management provided by 2 fieldwork

coordinators hired directly by SI. Supervisors were required to be in the field with the enumerators

on a regular basis and occasionally conduct random, unannounced spot checks to ensure that

enumerators were showing up for fieldwork, following the correct tracking protocols, and

administering surveys properly. The data collection firm also retained back checkers or auditors

who conducted call backs on a subset of the surveys every week. Whenever a high number of

discrepancies were found between the original survey and the call back, enumerators were given

warnings and re-training three times before being relieved from work. It was observed that the

number of discrepancies went down significantly after the first few initial rounds of audits,

indicating that the enumerators became more careful after realizing that falsification of data would

lead to disciplinary action.

With regard to targeting the Head of Household as the primary respondent for the household

roster, it was revealed that these individuals were often incapacitated, or in several instances,

elderly or illiterate. Furthermore, enumerators found that in some cases, the Head of Household

was not best suited to answer questions related to employment because the cultural definition of

Head of Household in Kosovo often translates to the oldest male in the household. Therefore, the

most suited individual to complete the roster was not necessarily the Head of Household, but a

younger person who was more knowledgeable about the employment outcomes of all household

members. A pre-screening was conducted prior to the roster to identify which members were the

most knowledgeable, and at least two attempted visits were made before replacing the most

knowledgeable respondent with the next most knowledgeable respondent.

Lastly, the listing exercise that was conducted prior to data collection in sampled enumeration

areas proved to be very beneficial to enumerators when attempting to locate the sampled

households. They found that high quality sketch maps (which were produced during the listing

exercise) were crucial to successfully reaching their interview destinations. It was validated that

collecting the Head of Households’ name, address, geocoordinates and phone number during

listing was also integral to locating the correct households.

SOCIALIMPACT.COM 25


INTRODUCTION

Kosovo, which gained independence from Serbia in 2008, is a relatively young country with a

history of ethnic tension and geopolitical unrest. These conditions have contributed to economic

underdevelopment, including high unemployment, increased dependency on the international

community and remittances. This creates an environment in which it is difficult to produce high

quality economic data—particularly labor force and time use statistics.

While labor force and time use data present a critical piece of a country’s overall macroeconomic

profile, the quality of survey data collected to-date in Kosovo has come under scrutiny for several

reasons, including the contracting of data collection on a lowest-cost basis, the compensation of

enumerators on a per-survey-completed basis, the potential response bias in respondents

reporting income, and the broader challenges associated with measuring employment (including

accurately capturing unpaid family workers, informality and seasonal agricultural employment).

One of the primary objectives of this study is to improve the quality of economic data in Kosovo.

As such, it is important to situate the proposed study within existing data and national statistics

on labor force and time use. Per the results of the 2016 Kosovo Labor Force Survey, the national

unemployment rate in Kosovo was extremely high at 27.5% percent. In terms of gender dynamics

in the labor force, working-age women were 5.6% more likely to report being unemployed than

men (31.8 and 26.2 percent, respectively) P2FP2FP2 FP2FP2FP2FP2 FP2F

3PPPPPPPP. Youth unemployment was also very high, with 52.4%

of active labor force participants identified as unemployed (ages 15-24)P3FP3FP3FP3FP3FP3FP3 FP3F

4PPPPPPPP. Among respondents

who were employed, 94 percent reported working full-time, and the net salary of most employees

fell between €300 and €400 per monthP4F. PPPPPPP

PPPPPP4FP4FP4FP4FP4FP4 FP4F

5PPPPPPPP As of 2014, Kosovo’s citizens have been identified as

the poorest in Europe, with a per capita GDP of just $8,000P5F. P5FP5FP5FP5FP5FP5FP5 F

6PPPPPP

In order to facilitate reliable economic analysis to inform growth-oriented programming, the

Millennium Challenge Corporation (MCC) commissioned a study to explore dynamics around

employment and time use among Kosovo’s resident population. Kosovo was selected as eligible

for Compact Development in December 2015, and a National Coordinator and Chief Economist

of the Core Team were appointed in May 2016. MCC and Core Teams have begun the process

of identifying binding constraints to growth, and a series of consultations with a variety of

stakeholders (including government officials, civil society organizations, and the private sector)

were held in June 2016. To further MCC’s objectives, MCC and Social Impact (SI) signed a

contract on September 29, 2016 valued at US$814,573 to conduct a nationally and regionally

3 Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics, 2017, Labour Force Survey

2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf 4 ibid 5 ibid 6 CIA (2016) ‘Kosovo’, The World Factbook.

SOCIALIMPACT.COM 26


representative labor force and time use survey in Kosovo in 2017. This research report presents

the research design and key findings from the study as well as provide comparisons to other

existing sources of employment data in Kosovo and similar countries.

SOCIALIMPACT.COM 27


REPORT OBJECTIVES

The main objective of this report is to present findings from the MCC Labor Force and Time Use

Survey (LFTUS). This report outlines the main research questions, presents the research

methodology and provides an overview of the key findings from the survey data, providing

comparisons to other existing sources of employment data in Kosovo and similar countries. In

order to explain the research methodology, this report provides an overview of the sampling

strategy, data collection tools and protocols, and measures taken to ensure data quality. The

findings section highlights the key descriptive statistics for employment and time use data, while

ensuring a specific focus on gender issues. Finally, the report outlines data protection and

respondent privacy plans, findings dissemination plans, team roles and responsibilities, and the

survey budget.

SOCIALIMPACT.COM 28


LITERATURE REVIEW

This literature review summarizes the status of the labor market and unemployment in Kosovo,

and assesses the perceived credibility of labor force data collected in the country. This review

includes sources from the Kosovo Agency of Statistics (KAS), the World Bank, the International

Labor Organization (ILO), Eurostat, the United Nations (UN), and various journals on international

economics. Databases used for this review include EconLit, Social Science Research Network,

OECD iLibrary, and more.

Kosovo’s labor market is underdeveloped and has room for improvement in terms of

macroeconomic and employment outcomes. Unemployment is high in Kosovo, with most sources

citing the 2016 KAS Labor Force Survey (LFS) figure of 27.5% percentP6F. P6FP6FP6FP6FP6FP6FP6 F

7PPPPPPPP While discouraging,

2016 shows improvement from past years; a World Bank study found unemployment was 48

percent in 2008–the year of Kosovo’s UN-recognized independence from Serbia P7F.P7FP7FP7FP7FP7 FP7FP7F

8PPPPPPPP In terms of

other key indicators, the labor force participation rate is calculated at 38.7percentP8F. P8FP8FP8FP8FP8FP8FP8F

9PPPPPPPP

Certain Kosovar subpopulations have felt the effects of low employment and a stagnant market

more than others, particularly women and youth. The 2016 KAS LFS results note large gender

differences in the labor market, with the labor force participation rate among working age women

at 18.6 percent compared to 58.3% for malesP9F. P9FP9FP9FP9FP9FP9FP9 F

10PPPPPPPP The ILOSTAT database reports a similar gender

gap, calculating the labor force participation rate among women at 16 percent compared to 51

percent for menP10F. P10FP10FP10FP10FP10FP10 FP10F

11PPPPPPPP Long-term youth unemployment has also been shown to be exceedingly high,

reported at 39.7 percentP11F.P11FP11FP11FP11 FP11FP11FP11F

12PPPPPPPP

A study central to both this literature review and the proposed research is the Kosovo LFS, a joint

publication between the World Bank and KAS that has been conducted on a near-annual basis

since 2001P12F. P12FP12FP12FP12FP12FP12 FP12F

13PPPPPPPP

7 Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics, 2017, Labour Force Survey 2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf.

8 World Bank (2010) ‘Improving Labor Market Outcomes in Kosovo’, Unlocking Growth Potential: Strategies, Policies, Actions – A Country Economic Memorandum. 9 Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics, 2017, Labour Force Survey 2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf.

10 Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics, 2017, Labour Force Survey 2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf.

11 ILO (2016) ‘Kosovo’, Country Profiles. 12Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics, 2017, Labour Force Survey 2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf.

13 Beginning in 2011 KAS introduced several revisions to their LFS methodology in order to better align their approach

with Eurostat guidelines, including shifting from a cross-sectional survey to a longitudinal rotating panel in which each household is visited four times per year, with one-quarter of the sample being replaced or rotated out each quarter.

SOCIALIMPACT.COM 29


The survey collects data in four main social and economic areas: macro-economic monitoring,

human resource development policies, employment policies, and income support and social

programs. Overall, the survey was designed to collect data pertaining to employment in Kosovo

and reports a variety of statistics that indicate labor force underdevelopment on a national scale.

This survey is the primary resource for labor force data in Kosovo and was notably cited in

published entries in the European Journal of Economics as well as the International Journal of

Economics, Commerce, and Management. As the LFS is in part conducted by the World Bank, it

is unsurprising to see many Bank publications incorporating LFS data into policy reports. For

example, a regional economic recovery report published by the Bank’s Central Asia Bureau used

Kosovo LFS data to help inform policy recommendations for the broader region.

While there is a substantial amount of literature that references data produced by the KAS LFS,

there are also publications that identify the data as low-quality or insufficient. For example, a

report published by the University of Pristina discredits data collected in the KAS Labor Force

Surveys, stating that labor force data in Kosovo up to the date of the publication have been very

“unreliable” P13F.P13FP13FP13FP13FP13 FP13FP13F

14PPPPPPPP Additionally, a somewhat dated World Bank study (2003) used LFS data to provide

an “adjusted” employment rate, correcting for the seasonality of the survey in order to provide a

more accurate labor market projection for the years ahead P14F. P14FP14FP14FP14FP14FP14FP14 F

15PPPPPPPP The widespread use of KAS LFS

data in publications, coupled with aforementioned validity concerns, indicates demand for high

quality, reliable statistics.

Lastly, much of the literature relating to the Kosovar labor market has been prescriptive, meaning

that it analyzes the existing state of employment in the country and uses it to make policy

recommendations to spur growth in the future. These recommendations varied greatly; a 2008

World Bank study recommends the implementation of well-functioning, female-focused labor

market regulations and more sufficient education/training in order to improve the state of the

Kosovar labor marketP15FP. P15FP15FP15FP15FP15FP15 FP15F

16PPPPPPP In a paper promoting solutions for post-recession economic recovery in

six Southeast European countries, authors recommend a broad set of interventions focused on

both the demand and supply sides of the labor market be implemented in the region P16F. P16FP16FP16FP16FP16FP16FP16F

17PPPPPPPP All papers

hope for better labor market outcomes in Kosovo’s future.

In addition to employment statistics, time use data are essential to gaining a more robust view of

the status of the labor market in Kosovo. The United Nations Economic and Social Council refers

to time use statistics as having value “not only for issues related to gender, but also more broadly

14 Nimani, S. A. (n.d.) Human Capital and the Labor Market in Kosovo. 15 World Bank (2003) Kosovo Labor Market Study: Policy Challenges of Formal and Informal Employment. 16 World Bank (2008) Kosovo Youth in Jeopardy: Being Young, Unemployed, and Poor in Kosovo. 17 Adugna, A., et al. (2013) ‘From Double Dip Recession to Fragile Recovery’, South East Europe Regular Economic

Report.

SOCIALIMPACT.COM 30


for quality-of-life concerns, social accounting, care of the elderly, estimates of the workforce and

total work accounts”.P17FP17FP17FP17FP17FP17FP17FP17 F

18

Among the most comprehensive sets of best practices in conducting time use research are

Eurostat’s 2008 Guidelines for Harmonised European Time Use Surveys. P18FP18FP18FP18FP18FP18FP18 FP18F

19PPPPPPPP The mixed methods

input and output harmonization approach outlined by Eurostat consists of a diary format,

specifications for data collection, and a coding list of common activities. Specific aspects of survey

design and practice are left up to the national statistics agencies of each respective European

country; in the context of Kosovo, the relevant organization is KAS, though the agency has yet to

produce a survey that explicitly focuses on collecting time use data.

Time use studies are subject to a number of challenges, including recall bias and the handling of

multi-tasking. To minimize these issues, the harmonized Eurostat approach recommends using

a real-time, self-administered diary whereby respondents record activities in 10-minute intervals

for two randomly designated days (one-week day and one weekend day). Respondents record

their activities in their own words, and the process of randomly selecting a week day and weekend

day to record time use takes place over the span of a year, preferably including potentially

“problematic” days (national or religious holidays). Respondents also record if other persons are

present and the location where their activities take place. The United Nations Department of

Economic and Social Affairs (UN DESA) similarly outlines this methodology for time use data

collection, referring to it as a “full” diary. UN DESA also describes another relevant survey

instrument - the “light” time diary (also referred to as a stylized diary or estimate), which directs

respondents to “report the time at which each activity occurs based on an exhaustive list, in other

words the 24 hours of the day are accounted for in terms of the identified activity categories.”P19FP19FP19FP19FP19 FP19FP19FP19 F

20PPPPPPPP

The list of activity categories may be broad (such as “employment” and “household and family

care”) or may contain more detailed tasks to be coded (including “food management” or

“studying”). Instead of relying on individuals to enter their information in an open-response format,

many stylized diary approaches use a limited list of fixed activity codes to categorize time use in

order to increase efficiency of data collection.

The choice between using a full and light diary to collect time use data depends on many factors,

including the “analytical objectives of the survey, available resources, literacy concerns, and

international comparability.” P20FP20FP20FP20FP20 FP20FP20FP20F

21PPPPPPPP According to a variety of studies conducted in Finland, Australia,

18 United Nations Department of Economic and Social Affairs: Statistics Division. Guide to Producing Statistics on Time

Use: Measuring Paid and Unpaid Work. Rep. no. ST/ESA/STAT/SER.F/93. New York: United Nations, 2004. Print 19 Eurostat. Harmonised European Time Use Surveys: 2008 Guidelines. Rep. no. KS-RA-08-014-EN. Luxembourg:

Office for Official Publications of the European Communities, 2009. Print. 20 United Nations Department of Economic and Social Affairs: Statistics Division. Guide to Producing Statistics on Time Use: Measuring Paid and Unpaid Work. Rep. no. ST/ESA/STAT/SER.F/93. New York: United Nations, 2004. Print. 21 ibid

SOCIALIMPACT.COM 31


and the United States, the two methods “often reveal roughly similar patterns of variation between

different groups.”P21FP21FP21FP21FP21FP21 FP21FP21F

22

While the Eurostat harmonized approach has been proven to produce reliable data that is

comparable across countries, is lengthy and expensive, and in research contexts where time and

budget are limited, a retrospective stylized or “light” diary can offer an attractive alternative. Given

the relatively limited scope and period of performance of the MCC Kosovo Labor Force and Time

Use Study.

22 Kan, Man Yee, and Stephen Pudney. Measurement Error in Stylised and Diary Data on Time Use. Working paper no. 2007-03. Oxford: Institute for Social and Economic Research, 2007. Print.

SOCIALIMPACT.COM 32


RESEARCH DESIGN

Research Questions & Key Variables

This study was designed to answer nine research questions. The first five were identified in the

RFQ, whereas the remaining four were added after MCC held additional stakeholder workshops

in Pristina.

1. What is the true level of employment (formal and informal), underemployment or

vulnerable employment, and unemployment, disaggregated by gender and district?

How much time have the un- or under-employed spent looking for a job? Are they an

entrepreneur?

2. Is there a relationship between field of study and having employment? How many

people are employed in the field that they studied? Are there any correlations

between certain fields of study or location and unemployment?

3. For people not looking for jobs, why are they not looking?

4. What are people doing with their time? Especially, what are women or the

unemployed doing with their time? How much time is spent on child care and elder

care? How much time is spent commuting?

5. How does receiving remittances and/or the ability to work abroad impact labor

market decisions?

6. How do attitudes about women's place in family and society influence women's

participation in the labor market?

7. How is women’s engagement in productive activities (like starting a business) related

to ownership and ability to control assets/access to credit?

8. Have discriminatory practices in hiring or while employed, or perceptions that there

might be such practices, discouraged women from seeking jobs?

9. What are the factors that inhibit entrepreneurship, especially for women vs. men, and

lead to the low levels of entrepreneurial activity for men and/or women?

Table 1 represents the key outcomes of interest for addressing the research questions stated

above.

Table 1: Research Outcomes of Interest

Key Outcomes Other Outcomes of Interest

Employment

Rate

• Sex

• Regions

• Rural/urban

• Age groups

• Education level attained

SOCIALIMPACT.COM 33


Key Outcomes Other Outcomes of Interest

• Field of study

• Occupation

• Economic activities

• Employed in an occupation corresponding to educational background

• Underemployment

• Vulnerable employment

• Informal employment

• Formal and informal business activity

• Remittance recipient

Unemployment

Rate

• Sex

• Regions

• Rural/urban


• Field of study

• Occupation

• Age groups

• Duration of unemployment

• Time spent looking for a job

• How unemployed persons particularly women spend their time

• How much time the unemployed women spend in childcare and adult care

• How much time unemployed women spent commuting

• By remittance recipients

Labor Force

Participation

Rate

• Sex

• Regions

• Rural/urban


• Field of Study

• Age groups

• Occupation

• Reasons for not seeking for a job

• Remittance recipients

Time Use

• Sex

• Urban/Rural

• Employed/Unemployed

• Active/Inactive

Gender Norms

• Urban/Rural

• Region

• Education Level

Research Design Overview

The research team administered a nationally and regionally representative household survey to

answer the nine questions outlined above. A stratified two-stage probability sampling plan was

SOCIALIMPACT.COM 34


used, with primary sampling units as census enumeration areas (EAs) and secondary sampling

units as households.

Survey respondents included heads of household (HOH)P22FP22FP22FP22FP22FP22 FP22FP22F

23PPPPPPPP or the most knowledgeable person

as well as one randomly selected female and one randomly selected male per household, both

of whom were non-students aged 18-74 years. Table 2 shows the breakdown of the of the survey

instruments that were administered to each selected household.

Table 2: Breakdown of Survey Instruments

Instrument Target Respondent Key Information

Household

Roster

Head of household (or person most

knowledgeable on the labor force

status of household members)

Demographics and employment data for each

household member above the age of 15

Extended

Interview

One randomly selected male and one

randomly selected female per

household between the ages of 18-74

Demographics, labor force participation,

entrepreneurship, time use, credit constraints,

asset ownership, workplace discrimination and

gender norms

Time use

Diary

Same respondent as Extended

Interview

Light diary of activities for the most recent 24-

hour period (4:00 AM – 3:59 AM)

The survey was cross-sectional, with data collection occurring over a 17-week period between

March 31, 2017 and August 4, 2017 Kosovar labor trends vary heavily in the summer and fall

months, and while data collection was initially intended to occur in the winter and spring months

to avoid seasonality in the data, it launched slightly later than expected, meaning that a portion of

data collection also took place during the summer season. Given that the data collection covered

both spring and summer months, this gave us the ability to look at seasonal variation in

employment in Kosovo.

The timing of field work was organized such that (a) data collection occurred simultaneously in all

seven regions and (b) each EA within a given region was randomly assigned to a two-day block

in which data collection would occur. This was done in order to ensure that the “light diary”

approach for the time use survey—which requires respondents to reflect and report on time use

for the previous day—captures the variations in time use over the span of a typical week

(specifically, weekdays and weekends). In addition, this ensured that variation in time-use and

seasonality was randomly distributed across regions and EAs within regions.

23 Consistent with other surveys in Kosovo (including KAS LFS), the head of household will be defined as the person who is recognized by other members as the head. In instances where this is not clear, the head of household will be considered the person who carries the main responsibility for the affairs of the household (chief decision-maker), is the chief breadwinner, and/or the rent or ownership of the dwelling is in his/her name.

SOCIALIMPACT.COM 35


Measurement Approach

In order to ensure the incorporation of the most recent literature available on the design and

implementation of a nationally representative labor force and time use survey, SI consulted three

core resources when informing the LFTUS instruments and methodology. The study’s

measurement approach synthesizes information from the Eurostat Guidelines on Harmonized

European Time Use Surveys, the Global Entrepreneurship Monitor, and the Gender Norm

Attitudes Scale, applying each tool where relevant. These resources are summarized below, and

will be described in greater detail in Section 12.3 of this report.

The Eurostat Method

The Eurostat approach has emerged as the leading labor force survey implementation protocol,

and is applied in this research context to ensure the collection of high quality data that fully

captures the study’s research questions on employment outcomes. The Eurostat method was

selected for LFTUS appropriation for its record of producing high quality labor force data in various

European research contexts, and it distinguishes itself from other approaches by emphasizing

comparability across time and countries. The Eurostat method provides a toolkit for conducting

surveys with large samples among private households in Europe and survey results are an

important source for European statistics about the situation and trends in the EU labor marketP23F. P23FP23FP23FP23FP23FP23FP23 F

24PPPPPPPP

The approach and its methodology as applied to the LFTUS is detailed in Section 5.2 of this

report.

Eurostat Harmonized European Time Use Surveys

Among the most comprehensive sets of best practices in conducting time use research, the

Eurostat guidelines on harmonized European time use surveys detail an approach that prioritizes

data quality and international comparability during the collection of time use-related survey data.

The mixed methods input and output harmonization approach outlined by Eurostat consists of a

diary format, specifications for data collection, and a coding list of predetermined common

activities. Specific aspects of survey design and practice are left up to the national statistics

agencies of each respective European country. In the context of Kosovo, the relevant organization

is the Kosovo Agency of Statistics (KAS), though the agency has yet to produce a survey that

explicitly focuses on collecting time use data. The Eurostat method and its adaptation for the MCC

Kosovo LFTUS are further detailed in the Section 12.3 of this report; for additional information on

the approach and its application, refer to the 2008 Guidelines on Harmonized European Time Use

Surveys P24F. P24FP24FP24 FP24FP24FP24FP24 F

25PPPPPPPP

24 Eurostat. “EU Labour Force Survey.” EU Labour Force Survey - Statistics Explained, 18 Sept. 2017,

ec.europa.eu/eurostat/statistics-explained/index.php/EU_labour_force_survey. 25 Eurostat. Harmonised European Time Use Surveys: 2008 Guidelines. Rep. no. KS-RA-08-014-EN. Luxembourg: Office for Official Publications of the European Communities, 2009. Print

SOCIALIMPACT.COM 36


Global Entrepreneurship Monitor

The Global Entrepreneurship Monitor (GEM) is an initiative designed to measure individuals’

perceptions of entrepreneurship and their involvement in entrepreneurial activity (both actual and

aspirational). In order to differentiate itself from other data sets that measure new business

registration, GEM focuses on the ‘individual as the embodiment of entrepreneurship’ P25FP25FP25FP25FP25FP25 FP25FP25F

26PPPPPPPP, and takes

a comprehensive socioeconomic approach to measuring entrepreneurship within a country by

identifying different types and phases of business development. The three major components of

GEM-measured entrepreneurship are identified as attitudes, activities, and aspirations; all three

of these facets are captured by the MCC Kosovo LFTUS’ survey instruments and are described

in greater detail in Section 12.3 of this report.

Compendium of Gender Scales: Gender Norm Attitudes Scale

Considering the MCC Kosovo LFTUS’s emphasis on capturing gender dynamics within the

country’s workforce, it was imperative that SI consult the Compendium of Gender Scales, a

compilation of tools used to assess gender-related attitudes and beliefs, during the design of the

study’s methodology and instruments. A specific component of the Compendium that proved

relevant to the LFTUS’ research questions is the Gender Norm Attitudes Scale (GNAS), which

aims to ‘measure egalitarian beliefs about male and female gender norms’ P26FP26FP26FP26FP26FP26 FP26FP26 F

27PPPPPPPP by capturing

respondents’ perception of gender norms through their reaction to a variety of statements related

to female education and labor force participation. As with the previous two resources, the

incorporation of the GNAS into the study’s instruments is further described in Section 12.3 of this

report.

Study Sample

Sampling Unit

The primary sampling unit (PSU) for the purpose of this survey is the group of geographic

Enumeration Areas (EAs) as defined by the 2011 Population and Housing Census. The unit of

analysis was individuals in sampled households within the selected EAs.

Sampling Strategy

This study employs a multi-stage stratified cluster randomized sampling methodology to arrive at

a representative sample for each of Kosovo’s seven regions. Sample size was calculated for each

region and then the EAs were stratified in accordance with the proportion of households in the

region that are urban/rural. Households were then selected within EAs using simple random

sampling.

26GEM 2016/2017 Global Report . Global Entrepreneurship Monitor Consortium, 2017,GEM 2016/2017 Global Report , gemconsortium.org/report/49812. 27Nanda, Geeta. 2011. Compendium of Gender Scales. Washington, DC: FHI 360/C-Change.

SOCIALIMPACT.COM 37


As an extension of the study’s sampling strategy, the research team decided to interview one

male and one female to in order to capture differences in employment and labor market outcomes

by sex. However, due to realized non-response rates, the actual distribution of respondents

interviewed was 60 percent female and 40 percent male. To achieve equal weights for male

and female household members, the respective weight of the male or female respondents was

adjusted based on the response rate in the following way: (60/50) * weight of the male respondent

and (40/50) *weight of the female respondent.

Sampling Frame and Selection

Enumeration Areas (EAs) - There are 4,626 EAs in Kosovo and data for the sampling frame

already existed at the Kosovo Agency of Statistics (KAS). The study team worked with KAS to

randomly select EAs in accordance with the power calculations.

Households - The sampling frame of households within EAs was constructed through a household

listing exercise. The October 2016 Design Trip revealed that household-level details within EAs

have not been updated since the 2011 Population and Housing Census. A rural EA household

listing was done in 2014 for the Agricultural Census, however urban EAs were excluded, as was

the region of North Mitrovica. To ensure the household listing is up to date and selection methods

are consistent across regions, our data collection partner, IDRA conducted a new household

listing exercise in each of the selected enumerations areas prior to the commencement of

fieldwork.

The SI team worked with IDRA to develop an abridged “quick count” listing instrument to meet

the primary needs of facilitating simple random sampling of households while maximizing

efficiency and limiting demands on respondents. P27FP27FP27FP27FP27FP27 FP27FP27F

28PPPPPPPP Household listings were conducted by teams

of two to four enumerators per EA using electronic tablets. EA maps were secured from KAS so

that geographic boundaries of the listing exercise were clearly delineated. For the purpose of this

study, a household was defined as a single person or group of persons (related or unrelated) who

share in household expenses spent on providing the household members with food and other

essential items for living. Members of the group may pool their incomes to a greater or lesser

extent. Shares in household expenses include benefiting from expenses (e.g., children or persons

with no income) as well as contributing to expenses. For each household fitting this definition,

enumerators registered one geo-tag and completed a short questionnaire recording all

households that resided within a single dwelling or structure. With a full updated list and map of

households in selected EAs, the study team then used Stata to carry out simple random sampling

to select households within the EAs.

28 While the study team captured the address/landmarks, geotag, number of households, and number of persons per household in every dwelling to facilitate simple random sampling of households as well as the application of sampling weights, it was not required that this information be reported directly by a household member. When household members were not home, for example, enumerators captured the required listing information from neighbors.

SOCIALIMPACT.COM 38


Sample Size

To calculate the required sample per region, the research team used the 2015 Kosovo Agency of

Statistics (KAS) Labor Force SurveyP28FP28FP28 FP28FP28FP28FP28 FP28F

29PPPPPPPP to (a) determine prevalence rates for key indicators of

interest including unemployment, entrepreneurship, domestic workers, discouraged workers, and

tertiary education and (b) calculated intra-cluster correlations on the aforementioned variables to

inform design effects.

The research team first determined the number of households to be sampled per cluster which

required considering trade-offs between statistical precision and logistical and practical

considerations related to field work (including duration and cost). There were considerable

diminishing returns to sampling additional households per EA and given the fixed costs associated

with sampling additional EAs both in terms of the household listing exercise and travel, SI took a

balanced approach and decided to target 10 households per EA.

SI then calculated the regional sample size required for each of the primary indicators and picked

the most conservative sample size of 122 EAs and 1,220 households per region for a total national

sample size of 854 EAs and 8,540 households.

Primary Data Collection Details

Instruments

The study used computer-assisted personal interviewing (CAPI)-enabled household surveys for

the Household Roster and the Extended Interview. The time use survey was conducted using a

paper diary to record the respondents’ activities between the most recent 24-hour period (4:00

AM to 3:59 AM) in 15-minute segments. The enumerators then categorized the activities into fixed

activity codes specified by the Eurostat Harmonized Time Use Approach and entered them into

a CAPI-enabled form. A paper diary was used to so that all activities could be recorded in detail

and to allow enumerators time to properly code the activities into pre-defined Eurostat codes. This

also allowed the research team to keep a record of the actual activities recorded at the time of

interview, so that any discrepancies in coding discovered during data quality monitoring could be

cross-checked with the paper diary. The household roster and the Extended Interview took, on

average, 30 minutes to complete, respectively. To compensate households for their time, they

were provided with a gift valued at approximately $3.25 per household for completing the roster

and the extended interviews.

Respondents

A pre-screening was conducted prior to administering the Household Roster, listing all members

of the household above the age of 15 to determine their eligibility for completing the roster as well

29 Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics , 2017, Labour Force Survey

2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf.

SOCIALIMPACT.COM 39


as the Extended Interviews. At the end of the pre-screening, the survey showed three screens,

one with a list of knowledgeable members to complete the roster, and one each with a randomized

list of members to complete the male and female Extended Interviews, respectively.

The primary candidate for completing the Household Roster was the Head of Household,

however, in cases where the household identified another member of the family as being more

knowledgeable about the labor force outcomes of all household members, preference was given

to that member for completing the roster to ensure that the roster data is as accurate as possible.

The Extended Interview was administered to one randomly selected female and one randomly

selected male household member as identified by the pre-screening. The Extended Interviews

were conducted simultaneously by teams of enumerators (one male and one female paired based

on gender). To limit response bias, enumerators explicitly asked to speak with respondents in

private and to conduct the interview in a private room or location outside of earshot of bystanders.

If interviews could not be done in private, enumerators marked who else was present or whether

there were any interruptions during the interview.

Survey Team

Surveys were implemented by enumerators from a local data collection firm, IDRA, and

enumerators were instructed to approach data collection activities in a culturally appropriate

manner and in the local language (Albanian, Serbian or Turkish). For surveys administered in

Serb Kosovar areas, the survey firm employed enumerators with the appropriate ethnicity.

Primary data collection was completed by 64 enumerators (16 teams of 4) hired and overseen by

IDRA. Enumerators were gender-balanced and were selected from IDRA’s network of 300

individuals. Given the security issues in North Mitrovica, only Kosovar Serb enumerators and

supervisors conducted fieldwork in that region. Enumerators were overseen by supervisors, such

that there was one supervisor for every 8 enumerators. In addition, supplementary management

and oversight was provided by 2 fieldwork coordinators. Finally, the data collection firm retained

4 back checkers or auditors who conducted call backs on a subset of the surveys.

Data Quality Assurance

Given the high impact potential of this study, it is of utmost importance that the data collected by

this survey is of very high quality. Several data quality assurance protocols were put in place to

ensure the integrity of the data as described below:

Translation – In order to ensure that the non-English tools were accurately capturing the nine

research questions, the original English tools were translated into Albanian, Serbian, and Turkish

and were subsequently back translated into English. Back translation was completed by

translators who were fluent both in English and the translated language and had no prior

knowledge of the tools. Any inconsistencies in translation were then highlighted and corrected to

ensure that the questions are asked accurately and in a culturally appropriate manner.

SOCIALIMPACT.COM 40


Pre-Testing – The purpose of the pre-test was to verify the appropriateness and

comprehensibility of the questions as well as gauge the overall time taken to carry out the survey.

As expected, the pre-test revealed important and unforeseen issues with the survey, particularly

around protocol, language, and concept definition. The pre-test took place over two days; the first

half of day one included training small teams of enumerators on the instruments and engaging in

role play followed by a field-based pilot exercise during the second half of the day. After the pilot,

an in-depth debrief was conducted with enumerators to inform the revision of the instruments.

Enumerator Training – The training took place over a five-day period prior to the launch of data

collection. During training, enumerators were briefed on the objectives of the study so that they

better understood the rationale behind the survey and could relay its objectives to respondents.

The training also included a module on good practices/ethics for data collection as well as a

session that delineated the roles and responsibilities of enumerators, supervisors, field

coordinators, and others involved. Enumerators received an opportunity to practice survey

administration during the training (via role-play simulation) as well as in the subsequent piloting

of the tools in a field setting. Given the extra focus on data quality for this study, supervisors

received extensive training on the use and importance of data quality assurance methods, such

as accompaniments, daily debriefs, data review, and weekly reporting. IDRA engaged

approximately 10 percent more trainees than required for data collection and only top performers

were selected to participate in the study based on their performance on the final quiz and during

the pilot exercise.

Use of CAPI – The surveys were administered using CAPI, specifically the ODK software

programmed on tablets and hosted via a SurveyCTO online server. This ensured higher efficiency

(eliminating data entry) and higher data quality given the ability to program constraints, validation,

and data quality monitoring, as well as immediate access to the data while enumeration was

ongoing. SurveyCTO’s in-built data quality features include speed violations, module time stamps,

text audits, and soft check suppression were also incorporated for data quality monitoring. Using

CAPI also ensured protection of the data, as all data stored on the SurveyCTO server is encrypted

and access to the server was given only to authorized personnel who were listed on the IRB

application and had training in human subjects’ research.

Real-Time Data Monitoring – All data collected on the tablets was reviewed by SI staff on the

SurveyCTO online platform daily for the first two weeks of data collection and then tapered off to

being reviewed on weekly basis. The purpose of these checks was to proactively identify and

remedy issues related to enumerator error/performance, check question clarity, and reveal

undetected programming errors or areas for improvement. Specific checks covered by SI’s Stata

.do file template included date/time consistency, survey completion, duplicates, routing/logic

checks, variable distribution, “don’t know”/“refused” frequencies, “other” frequencies, outliers, and

enumerator fixed effects.

SOCIALIMPACT.COM 41


Accompaniments and Spot checks – Supervisors accompanied enumerators to a subset of

their interviews. During each accompaniment, the supervisors filled out an electronic

accompaniment form rating the enumerator’s performance. Based on their observations, the

supervisors gave enumerators feedback on their performance, re-trained them or took disciplinary

action when required. In addition, supervisors also made unannounced spot checks during data

collection in order to observe whether or not enumerators were present, administering the

interviews properly and following all protocols. Disciplinary action was taken in the form of re-

training, warnings and firing if enumerator performance was not up to the mark.

Backchecks – Backchecks were conducted on a subset of the interviews for each enumerator.

They were conducted both in-person and by telephone for attempted households as well as

successfully enumerated households. During these backchecks, households were asked whether

not they were approached by enumerators, who they were approached by, and whether they

completed the interview. They were also asked a subset of questions from the survey and data

from back checks was then compared to the original data collected by enumerators to check for

data falsification, mistakes, and need for potential retraining. Any time an intolerable number of

discrepancies were found, those surveys were re-done and the responsible enumerators were

given up to three warnings before being relieved from work.

Independent Monitoring – SI hired two data quality assurance specialists who engaged in field-

monitoring. Specifically, they conducted random spot checks, additional accompaniments, and

back checks, and participated in team debriefs.

SOCIALIMPACT.COM 42


FINDINGS

Demographics

The study implemented a nationally representative labor force and time use survey in 854 EAs in

all seven regions of Kosovo – Ferizaj, Gjakova, Gjilan, Mitrovica, Peja, Pristina, and Prizren. Over

a course of 17 weeks from the 31PPPPPPPP

stPPPPPPPP of March to the 4PPPPPPPP

thPPPPPPPP of August, 8,533 Household Rosters were

completed, gathering employment information about 32,742 individuals, out of which 31,386

individuals belonged to the working age population, the primary focus of the findings of this report.

In addition, 3,710 male Extended Interviews and 4,894 female Extended Interviews were also

completed. The research design agreed upon by SI and MCC targeted 122 EAs to be completed

per region. Table 3 below outlines the distribution of surveys completed across EAs by region,

which mirrors the distribution mandated in the study’s design.

Table 3:Distribution of surveys completed across EAs by region

Region Enumeration

Areas (EAs) Households

Male Extended

Interviews

Female Extended

Interviews

Ferizaj 122 1,220 573 739

Gjakova 122 1,220 600 791

Gjilan 122 1,219 349 513

Mitrovica 122 1,220 605 737

Peja 122 1,220 634 788

Pristina 122 1,215 479 642

Prizren 122 1,219 470 684

Total 854 8,533 3,710 4,894

The study was successful in completing a total of 10 household rosters per EA, with the exception

of 3 EAs in which 10 rosters could not be completed due to the small number of households

located in those EAs. The response rate for the original 10 households sampled in each EA was

approximately 75%, indicating that a healthy proportion of the study’s target population was

reached.

Kosovo, as a small Balkan state, is a country with a higher percentage of rural land than urban

settlements. The sample reflects this, with 42% of EAs identified as urban and 58% of EAs as

rural. Table 4 below displays the distribution of urban and rural EAs by region. It is worth noting

that, as the home to the nation’s capital, the only region to house more urban that rural dwellings

is Pristina. All estimates presented in this report are weighted according to the weighting strategy

outlined in Annex 2.

SOCIALIMPACT.COM 43


Table 4: Distribution of urban and rural EAs by region

Ferizaj Gjakova Gjilan Mitrovica Peja Pristina Prizren Total

Urban 46 46 48 56 50 65 47 358

Rural 76 76 74 66 72 57 75 496

Total 122 122 122 122 122 122 122 854

The size of the average household in the sample was 5 members, with the smallest household

containing only one member and the largest household containing 27 members. However, when

including only members eligible to participate in the LFTUS (i.e. individuals above the age of 15),

the average household size was approximately four members and the smallest household was

comprised of one member, while the largest was comprised of 16 members. There were, on

average, 2.3 male members and 2.3 female members in a given household. All statistics reported

in this report are based on outcomes of the working age population, which is defined by Eurostat

as those aged between 15 and 74 years.

The working age population sample was almost even in terms of its gender distribution; 15,745

females (50.2%), compared to 16,641 males (49.8%). The average age of the working age

population was 37.5 years (the male average was slightly lower at 37.2, and the female average

higher at 37.8). The majority of the sample was young, with 15 to 24-year-olds and 25 to 34-year-

olds comprising 28.7% and 20.8% of the sample, respectively. 31.8% of individuals were middle

aged, with 15.6% of the sample comprised of adults between ages 35 and 44, and 16.2% of the

sample aged 45-54. 18.7% of the working age population was elderly, with 11.9% of individuals

between the ages of 55 and 64 and 6.8% of individuals between the ages of 65 and 74.

11T11T11T11T11T11T11T11TThe majority of the working age population – 60.6% - identified themselves as either married or

in a factual relationship, meaning they had an established live-in partner. 35.6% of participants

were identified as single, and about 3.5% were widowed and only 0.2% were divorced.11T11T11T11T11T11T11T11T

A vast majority (approximately 99.4%) of individuals were citizens of Kosovo, while 0.6% of

individuals did not have Kosovar nationality. While Kosovo has many ethnic groups within its

population, the majority of individuals (91.6%) were identified as ethnically Albanian. The

remaining 8.4% of them were identified (in descending order) as Serbian, Bosnian, Gorani,

Turkish, Ashkali, Egyptian, Roma, other, Montenegrin, or Croat P29F. P29FP29FP29FP29FP29FP29FP29F

30PPPPPPPP

The level of education completed by most individuals in the working age population was lower

secondary schooling, at 36.6%, with the highest level of education obtained by majority of the

females being lower secondary, at 44.8%. The highest level of education completed by males

was upper secondary vocational training, at 38.1% (Table 5). The sample proved diverse in

participants’ type of schooling; 15.2% completed upper secondary general education, while 27.4%

completed upper secondary vocational education. Among lower educated respondents, 3.3%

30 The LFTUS breakdown of ethnicities mirrors that of the most recent census conducted in Kosovo.

SOCIALIMPACT.COM 44


reported not finishing primary school. Doctoral degrees were rare among the working age

population sample, with only 42 individuals holding such degrees.

Table 5: Highest Level of Education, by Sex

Highest Level of Education Male

(%)

Female

(%)

Total

(%)

Did not Finish Primary School 1.2 5.3 3.3

Primary Education 3.3 9.4 6.3

Lower Secondary Education 28.5 44.8 36.6

Upper Secondary – General 16.2 14.2 15.2

Upper Secondary – Vocational 38.1 16.6 27.4

Post Secondary – Vocational 3.1 1.4 2.3

Tertiary/University 7.9 7.2 7.6

Post Graduate/Master 1.5 1.0 1.3

Doctorate 0.1 0.0 0.1

Note: All estimates are significant at the 5% level.

There was a high level of variation in terms field of study, which was asked to a subset of

individuals who had completed at least upper secondary education (56.3% of the working age

population or 31,15 individuals). Popular fields of study included social sciences (18.8%), crafts

and trades (13.3%), engineering (12.4%), and general programs (12.1%), as displayed in Table

6. Social Sciences were the most popular among both males and females, with the biggest gender

disparity in the fields of crafts and trades and engineering (more popular among males) and in

the fields of health and welfare (more popular among females).

Table 6: Field of Study, by Sex

Field of Study Male

(%)

Female

(%)

Total

(%)

Social Sciences 16.2 23.1 18.8

Crafts and Trades 17.7 5.8 13.3

Engineering 17.7 3.5 12.4

General Programs 11.3 13.5 12.1

Physical Science (Including Physics, Chemistry, and

Earth Science) 7.3 11.0 8.7

Mathematics and Statistics 5.1 7.0 5.8

Health and Welfare 2.4 10.6 5.4

Humanities, Languages and Arts 3.5 6.5 4.6

Teacher Training and Education Science 2.4 5.8 3.6

Law 3.4 4.0 3.6

Agriculture and Veterinary 3.7 1.7 3.0

Computer Science 3.0 2.2 2.7

SOCIALIMPACT.COM 45


Field of Study Male

(%)

Female

(%)

Total

(%)

Manufacturing and Construction 2.5 1.4 2.1

Business 1.3 2.0 1.6

Life Science (Including Biology and Environmental

Science) 1.1 1.4 1.2

Services 1.0 0.0 0.7

Administrative and Clerical 0.4 0.6 0.5


The Eurostat Method

As mentioned above, this study followed the Eurostat methodology to collect high quality data to

determine labor market outcomes in Kosovo. This section explains the key Eurostat definitions

(Table 7) as relevant to the Kosovo Labor Force and Time Use Study, before delving into the

findings.

Table 7: Definitions of Eurostat Terms

Outcome Eurostat definition

Working age

population

All persons aged 15-74 in the population

Employment

Persons at least 15 years of age who during the reference week worked for

at least one hour for pay or profit for family gain or who were not at work

during the reference week, but had a job or business from which they were

temporarily absent (applicable to employees, self-employed persons, and

family workers).

Unemployment

Persons aged 15-74 years who were not employed during the definition of

employment, currently available for work, or actively seeking work (had taken

steps within the last four weeks to seek out paid employment.

Active population The population employed or unemployed (also referred to as the labor force).

Economically Inactive

Persons

Those who are neither employed nor unemployed.

Employment Rate Share of employed persons among the working age population (15-74), also

known as the employment to population ratio.

Unemployment Rate Share of unemployed persons among the labor force.

Underemployment Persons working part-time because they are unable to find full-time jobs.

Vulnerable

Employment

Share of persons in vulnerable employment (self-employed without

employees or unpaid family workers) among the employed population.

SOCIALIMPACT.COM 46


Given the complexity of the terms listed above, the Eurostat method stipulates that LFS

respondents not be directly asked if they are employed, unemployed, or inactive. Instead, survey

questions inquire about respondents’ labor market behavior during a given reference week, and

their labor force status is derived from their responses. This methodology is illustrated in the flow

chart below:

Figure 16: Eurostat Methodology (Diagram)

The derivation strategy described above and the appropriate skip logic was programmed into the

study instruments and then the aforementioned variables were constructed during the analysis

phase using the definitions listed above.

Employment

According to Eurostat, persons in employment are 11T11T11T11T11T11T11T11Tdefined as individuals “aged 15 years and over

and living in private households, who during the reference week did any work for pay or profit for

SOCIALIMPACT.COM 47


at least one hour, or were not working but had jobs from which they were temporarily absent” P30F.P30FP30FP30FP30FP30 FP30FP30F

31PPPPPPPP

Unpaid family workers are also considered to be employed according to Eurostat, which are

defined as individuals who performed any work during the reference week on a farm that is owned

or rented by themselves or a family member and the output produced from their work is either

intended to be sold or bartered or constitutes an important contribution to the total household

consumption. This definition was used to guide LFTUS instrument development, and all

references to employed individuals allude to persons who fit this description. The employment

rate is calculated as the share of employed individuals among the working age population (15-74

years), also known as the employment to population ratio.

Employment rate

The LFTUS results revealed that the aggregate employment rate stood at 41.1% at the time of

the survey, and there was a stark gender divide when it came to employment; 58.3% of males

were employed, compared to 24.1% of females (Figure 17). The employment rate for youth

(persons aged 15-24) was 27.7%, and nearly doubled for age groups 25-34, 35-44, and 45-54.

The employment rate for male youth, which stood at 39.3%, was more than twice the employment

rate of female youth, which was reported at 15.6%. When a logistic regression was run controlling

for other covariates, it was found that male youth were almost 4 times more likely to be employed

compared to female youth (Annex 3).

31 OECD Statistics Directorate. “Glossary of Statistical Terms: Employment (Eurostat Definition)”, OECD, 29 July 2002, stats.oecd.org/glossary/detail.asp?ID=779.

SOCIALIMPACT.COM 48


Figure 17: Employment Rate (%), by Age and Sex

Employment varied substantially by region – Gjakova residents reported the highest employment

rate at 49.5%, while Pristina, which houses the nation’s capital, surprisingly reported the lowest

employment rate at 36.5%. However, when the employment rates were disaggregated by urban

and rural areas within regions, it was found that the employment rate for urban areas in Pristina

was second highest at 40.8% and the employment rate for rural areas in Pristina was the lowest

among all regions at 34.2% (Table 8). The situation in Gjakova found the opposite, in which the

rural areas reported a substantially higher employment rate of 50.2%, compared to that of 43.7%

in urban areas. Both Pristina and Gjakova, showed the highest urban-rural disparity, showing a

6-percentage point difference in employment rates between urban and rural areas.

Table 8: Employment Rate (%), by Region and Geographic Location

Employment Rate (%)

Gjakova Gjilan Prizren Mitrovica Peja Ferizaj Pristina Total

Urban 43.7 39.5 38.5 38 36.9 35.8 40.8 39.3

Rural 50.2 44.1 43.4 43.5 39.4 37.3 34.2 41.7

Total 49.5 43.2 42.6 42.2 38.8 37.0 36.5 41.1


As expected, level of education showed a positive relationship with employment among

respondents; when employment levels were high, so were levels of education. Respondents who

did not finish primary school had employment rates below 14.4%, while individuals who reported

having a tertiary level of education or finishing post graduate or doctoral studies had employment

rates of 61% and above. Table 9 below displays the relationship between level of education and

39.3

70.474.6 74.4

61.4

20.2

58.2

15.6

28.332.4 31.5

23.5

8.7

2427.7

50.6 52.2 51.7

43.4

14.2

41.2

15-24 25-34 35-44 45-54 55-64 65-74 Total

Male Female Total

SOCIALIMPACT.COM 49


employment, disaggregated by sex. Notably, the data show a somewhat of a convergence of

employment rates between males and females at high and low levels of education.

Table 9: Employment Rate (%), by Highest Level of Education and Sex

Employment Rate (%)

Highest level of education Male Female Total

Did not finish primary school 19.4 13.3 14.4

Primary education 33.4 18 22

Lower secondary education 47.6 21.4 31.6

Upper secondary - general 59.9 21.6 42.1

Upper secondary - vocational 65.4 25.4 53.4

Post secondary - vocational 50.7 36.1 46.2

Tertiary/University 71.2 50.4 61.4

Post graduate/Master 79.1 73.2 76.8

Doctorate 77 77.1 77

Note: All estimates are significant at the 5% level

Employment also appeared to vary by field of study (which was only asked to individuals who had

completed at least upper secondary education); respondents who studied services reported

employment rates of 66.1%, followed by engineering at 64.7% and teacher training and education

science (61.6%) (Table 10). The most popular field of study, social sciences, reported the fifth

lowest employment rate among all fields, while the second most popular field of study,

engineering, had the second highest employment rate.

Table 10: Employment Rate (%), by Field of Study

Field of Study

Employment Rate

(%)

Services 66.1

Engineering 64.7

Teacher training and education science 61.6

Administrative and clerical 59.6

Crafts and trades 59.2

Agriculture and veterinary 58.5

Life science (including biology and environmental science) 56.9

Computer science 55.2

Humanities, languages and arts 51.9

Law 50

Business 47.9

Manufacturing and construction 47.6

Social sciences 46.9

SOCIALIMPACT.COM 50


General programs 46.7

Mathematics and statistics 44.8

Health and welfare 43

Physical science (including physics, chemistry, and earth science) 39.5

Total Employment Rate (for Individuals with at least upper secondary education) 51.5


Only one in three individuals reported their current or most recent job to be related to their primary

field of study. Figure 18 demonstrates the popularity of each field and the relationship between

related employment for the seventeen fields of study. The lowest relatability of jobs to field of

study was found to be in General Programs (8%), followed by Physical Science (15%), and

Mathematics and Statistics (27%). The highest relatability was found to be in Teacher Training

and Education (76%), Health and Welfare (62%) and Life Science (51%).

Figure 18: Proportion Working in a Job Related to their Field of Study, by Field of Study

Of the 66.2% individuals who indicated that their current or most recent job was not related to

their field of study were asked about why that was not the case. An overwhelming majority of

them, 91.9%, indicated that it was because there were no jobs available in their field of study.

0%

10%

20%

30%

40%

50%

60%

70%

80%

Proportion of Individuals in Field of Study Job Related to Field of Study

SOCIALIMPACT.COM 51


Occupation and Economic Activity

The occupations and sub-occupations were coded based on the International Standard

Classification of Occupations (ISCO) P31FP31FP31FP31FP31FP31 FP31FP31F

32PPPPPPPP used by the ILO. The survey asked employed individuals

about their sub-occupations, which were used to derive higher level categories for occupations,

as defined by ISCO guidance. The most common types of occupations among respondents were

elementary occupations, which include cleaners and food preparation assistants, agriculture and

construction laborers (30.2%), followed by services and sales workers (13.1%). The least

common occupations were technicians and associate professionals which include health,

business, legal, information and communication related associate professionals (1.2%) and

armed forces occupations (2.6%).

The distribution of males and females in elementary occupations was similar. Some occupations

varied more by sex; for instance, male respondents were more likely to hold craft and trade related

occupations (18.8%), compared to only 8.4% of females. In addition, plant and machine operators

are almost always male (6.6%) compared to only 0.1% of females who identified themselves as

being in these occupations. Conversely, females were twice as likely to be in professional

occupations such as health, teaching, business, science, and legal professionals (cumulatively,

19.2%), compared to 8.8% of males.

Men were substantially more likely than women to work in craft and trade sectors which include

building, metal works, electrical, woodwork, handicraft, food processing and related trades and

women were more likely to work in skilled agricultural, forestry and fishery sectors. When viewing

occupations by geographic classification, rural areas had a much higher proportion of elementary

occupations (34.6%) compared to urban areas (14.5%). Conversely, urban areas had a higher

share of services and sales workers (24.5%) and professionals (20.2%), the shares of which were

substantially lower in rural areas at 13.9% and 9.1%, respectively. Table 11 below displays the

distribution of occupation by broad category, disaggregated by sex and geographic location. For

disaggregations that include sub-occupations and job industry, see Annex 3.

Table 11: Occupation (%), by Sex and Geographic Location

Occupation Male

(%)

Female

(%) Urban

(%)

Rural

(%) Total

(%)

Elementary Occupations 29.1 33.4

14.5 34.6

30.2

Services and Sales Workers 15.9 17.3 24.5 13.9 16.3

Craft and Related Trades Workers 18.8 8.4 14.4 16.6 16.1

Professionals 8.8 19.2 20.2 9.1 11.5

Skilled Agricultural, Forestry and Fishery

Workers 6.9 11.2 1.2 9.9 8.0

Managers 6.4 2.7 10.1 4.2 5.5

32 “International Standard Classification of Occupations .” ISCO - International Standard Classification of

Occupations, International Labour Organization, 21 June 2016, www.ilo.org/public/english/bureau/stat/isco/isco08/.

SOCIALIMPACT.COM 52


Plant and Machine Operators and Assemblers 6.6 0.1 4.5 5.0 4.9

Clerical Support Workers 3.6 5.1 6.7 3.3 4.0

Armed Forces Occupations 2.6 0.1 1.1 2.2 2.0

Technicians and Associate Professionals 1.2 2.5 2.9 1.2 1.5


Additionally, occupations vary by region, with Gjakova reporting the highest share of elementary

occupations (52.5%) and Pristina reporting the lowest (23.5%). The highest share of services and

sales workers (21.3%) and professionals (16.1%) were in Pristina. The highest share of managers

was reported in Ferizaj (9.5%) while the lowest share of managers was in Mitrovica (2.7%). The

highest share of craft and related trade workers was reported in Gjilan and Prizren, about 23%,

and the lowest was in Pristina (10.5%). Table 12 shows the distribution of occupations by region.

Table 12: Distribution of Occupations (%), by Region

Occupation Ferizaj

(%)

Gjakova

(%)

Gjilan

(%)

Mitrovica

(%)

Peja

(%)

Pristina

(%)

Prizren

(%)

Total

(%)

Elementary

Occupations 24.8 52.5 25.9 25.8 28.2 23.5 27.0 30.2

Services and

Sales Workers 17.7 10.4 17.4 17.8 16.5 21.3 13.2 16.3

Craft and Related

Trades Workers 19.8 12.4 23.2 14.8 11.8 10.5 23.0 16.1

Professionals 9.3 6.7 13.1 12.3 11.9 16.1 10.1 11.5

Skilled

Agricultural,

Forestry and

Fishery Workers

6.0 5.8 4.9 9.5 12.2 3.6 14.8 8.0

Managers 9.5 3.7 3.7 2.7 7.3 8.4 3.1 5.5

Plant and

Machine

Operators and

Assemblers

6.3 3.3 5.2 6.0 5.2 5.2 4.4 4.9

Clerical Support

Workers 3.1 1.9 3.9 5.4 3.5 6.9 2.6 4.0

Armed Forces

Occupations 1.8 1.9 1.9 3.8 2.2 1.8 1.1 2.0

Technicians and

Associate

Professionals

1.7 1.3 0.9 1.9 1.2 2.7 0.7 1.5


Occupations varied by both level of education and field of study. Individuals with higher levels of

education appeared to hold higher-level positions such as managers and professionals. For

SOCIALIMPACT.COM 53


individuals who did not complete lower secondary schooling (the lowest tier of education), more

than half were employed in elementary occupations. The most common occupation for persons

with doctorate degrees was the professionals category, which captures teachers, doctors,

engineers, and business professionals. A full disaggregation of occupation by education level and

field of study is available in Annex 3.

The study also looked at the distribution of jobs in the different sectors (using NACE P32FP32FP32FP32FP32FP32 FP32FP32 F

33PPPPPPPP

classification codes), as reported in Table 13. The highest proportion of jobs was in the agricultural

sector, about 21.7%. This is commensurate with the share of agriculture in Kosovo’s economy

which is about 20%. Other popular sectors include Construction (13.9%), Other Service Activities

(13.7%) and Whole Sale and Retail Trade (9.3%). Both males and females have the highest

proportions in these sectors except for construction which is popular among males (18.6%), but

only 0.6% of females take part in this economic activity.

Table 13 Distribution of jobs in different sectors (%), by Sex

Economic Activity Male

(%)

Female

(%)

Total

(%)

Agriculture, Forestry and Fishing 17.6 33.3 21.7

Construction 18.6 0.6 13.9

Other Service Activities 13.5 14.3 13.7

Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles 8.9 10.6 9.3

Education 5.1 10.5 6.5

Manufacturing 6.4 4.6 5.9

Public Administration and Defense; Compulsory Social Security 5.4 2.0 4.5

Accommodation and Food Service Activities 5.2 2.3 4.4

Human Health and Social Work Activities 1.5 7.6 3.1

Transportation and Storage 3.8 0.3 2.9

Activities of Households as Employers; Undifferentiated Goods- and

Services-Producing Activities of Households For Own Use 2.0 4.6 2.7

Electricity, Gas, Steam and Air Conditioning Supply 3.2 0.3 2.5

Arts, Entertainment and Recreation 1.6 3.4 2.1

Administrative and Support Service Activities 1.3 1.2 1.3

Financial and Insurance Activities 1.0 1.3 1.1

Information and Communication 1.0 1.3 1.1

Mining and Quarrying 1.1 0.3 0.9

Water Supply; Sewerage, Waste Management and Remediation Activities 1.0 0.2 PPPPPPPP

NS 0.8

Professional, Scientific and Technical Activities 0.6 0.4 0.6

33 NACE (Nomenclature des Activités Économiques dans la Communauté Européenne) is a European industry standard classification system similar in function to Standard Industry Classification (SIC) and North American Industry Classification System (NAICS) for classifying business activities. http://www.tl9000.org/abcb/nace.html

SOCIALIMPACT.COM 54


Economic Activity Male

(%)

Female

(%)

Total

(%)

Activities of Extraterritorial organizations and Bodies 0.4 0.3 0.4

Real Estate Activities 0.1 0.1 PPPPPPPP

NS 0.1

Other 0.5 0.6 0.5

Note: All estimates are significant except for the ones marked with PPPPPPPP

NS

Types of employment (contractual/non-contractual, government/private)

The survey asked employed individuals whether they were employees, self-employed with

employees, self-employed without employees, or unpaid family workers. The majority of

individuals, about 60.8%, identified themselves as employees, while 23.0% of jobs were classified

as unpaid family work. The percentage of female unpaid family workers was more than double

that of male unpaid workers: 39.3% of females were unpaid family workers compared to 17.3%

of men (Figure 19). All estimates are significant at the 5% level.

Figure 19: Type of Employment (%), by Sex

Furthermore, the proportion of unpaid family workers in rural areas was 27.8%, compared to only

6.1% in urban areas (Table 14). Of all unpaid family workers, 72.2% were employed in Agriculture,

Forestry and Fishing sector. Regionally, the highest proportion of unpaid family workers was in

Gjakova (44.1%) and lowest in Pristina (8.8%). By education level, of those who completed only

primary education or lower, approximately 66% were unpaid family workers, and the data showed

likelihood to be working in unpaid family labor to be decreasing with higher levels of education.

For further dis-aggregations by age, education level and field of study, refer to Annex 3.

63.7

5.6

13.417.3

52.5

1.5

6.7

39.3

60.8

4.5

11.6

23.0

Employee Self-Employed WithEmployees

Self-Employed WithoutEmployees


Male Female Total

SOCIALIMPACT.COM 55


Table 14: Type of Employment (%), by Geographic Location

Type of Employment (%)

Employee

Self-Employed

with Employees

Self-Employed

Without Employees

Unpaid Family

Worker

Urban 76.1 6.4 11.4 6.1

Rural 56.5 4.0 11.7 27.8

Total 60.8 4.5 11.6 23.0


Individuals who indicated that they were employees, were asked whether the jobs they held were

contractual or not, a proxy for job permanence and quality. Approximately 59.2% of individuals

reported having contractual jobs. There was a pronounced difference in contractual employment

between males and females, with 71.9% of females reporting working under contract as

compared to 55.5% of males. The types of employers that offered the highest proportion of

contractual jobs were government and state-owned enterprises (more than 95%), while

contractual jobs offered by private companies were 44%. Table 15 below displays the distribution

of contractual employment among the sample.

Table 15: Percentage of Contractual Jobs, by Employer Type

Govt/

Public

Sector

State-

Owned

Enterprise

Private

Company

or

Enterprise

Intl.

Org NGO

Self-

Employed

Other

Private

Individual

Other

Contractual

Jobs (%) 97.9 95.9 44.4 84.6 81.5 9.1 7.4 65.1


Of the individuals who indicated that they were employees, the survey inquired about the type of

employer (private, governmental, non-profit, etc.) they worked for. According to findings, the

private sector provided more than half of all jobs (approximately 60%), with the government/public

sector emerging as the second largest employer, providing 25% of jobs. Both males and females

were more likely to be employed in the private sector, followed by the government sector (Table

16); when disaggregating findings by age, younger individuals were substantially more likely to

be employed in the private sector with the differential decreasing with increasing age. However,

the 55-64 age group was more likely to be employed in the government sector than the private

sector. There were only 31 individuals among the 65-74 age group who identified themselves as

employees, of which 18 were employed in the private sector and 8 in the public sector.

SOCIALIMPACT.COM 56


Table 16: Type of Employer (%), by Sex and Age Group

Type of Employer (%)

Govt/

Public

Sector

State-

Owned

Enterprise

Private

Company or

Enterprise

Intl.

Org NGO

Self-

Employed

Other

Private

Individual

Other

Sex

Male 23.2 5.4 63.8 1.0 0.4 1.0 5.3 0.0

Female 31.6 8.5 49.4 0.9 1.6 0.9 7.1 0.1

Age Group

15-24 4.4 1.1 83.7 0.2PPPPPPPP

NS 0.6 1.3 8.7 0.0

25-34 17.8 4.0 70.0 1.0 0.4 0.9 5.8 0.0

35-44 27.9 6.6 58.0 1.0 0.8 0.5 5.0 0.2

45-54 37.7 9.3 44.7 1.3 0.7 1.0 5.3 0.0

55-64 54.1 14.0 25.6 1.4 0.9 1.3 2.6 0.0

65-74 30.4 1.5PPPPPPPP

NS 59.6 0.0 4.5PPPPPPPP

NS 0.0 3.3PPPPPPPP

NS 0.7

Total 25.1 6.1 60.5 1.0 0.7 1.0 5.7 0.0

Note: All estimates are significant at the 5% level except for the ones marked with PPPPPPPP

NS

Net monthly salary

The average monthly salary of employed individuals, excluding unpaid workers, was €340, with

males earning €353 a month, on average, and their female counterparts earning €289, on

average. There was also a substantial amount of variation between sexes for the highest salary

earned; the highest earning male respondent reported a monthly income of €6000, while the

highest earning female made €2000 a month. Median income followed a similar pattern, with

males earning a median of €300 and females earning €250.

Regionally, net monthly income showed variation, with both male and female residents of Pristina

earning higher wages than their counterparts in the other six regions. Residents of Gjakova

reported the lowest incomes of any region, an average of €299 (Table 17), largely due to the

prevalence of agricultural jobs in this region. The largest gender disparity in income was found to

be in Gjakova, with males earning €121 more than their female counterparts. There was not a

significant disparity between the net monthly incomes of urban and rural respondents; urban

residents reported earning, on average, €14 more than rural residents, who earned monthly

incomes of €336.

Table 17: Average Monthly Salary, by Sex and Region

Average Monthly Salary (€)

Region Male Female Total

Pristina 379 311 360

Peja 353 312 344

Ferizaj 354 271 340

SOCIALIMPACT.COM 57



Region Male Female Total

Prizren 344 319 340

Gjilan 351 292 337

Mitrovica 341 266 328

Gjakova 328 207 299

Total 353 289 340


Job income also varied by type of employer, with employees of international organizations

receiving the highest salary at €789, followed by those working for state-owned enterprises at

€442. Government employees were next at €415, with the lowest salary received by those

working for other private individuals at €198.

Table 18: Average Monthly Salary (n), by Job Type


Govt/

Public

Sector

State-

Owned

Enterpris

e

Private

Company or

Enterprise

Intl. Org NGO Self-

Employed

Other

Private

Individual

Other

415 442 304 789 292 282 198 241


Regarding job sector; individuals working in international organizations earned the highest

monthly salary of €855, which is consistent with the breakdown shown by type of employer in

preceding sections. The next most high paying sectors were mining and quarrying (€485) and

professional technical and scientific activities (€459). Those working in Arts, entertainment and

recreation report earning the lowest salary €225. Table 19 below shows average monthly income

disaggregated by job sector and sex. Males earned higher salaries than females in all sectors

except Financial and Insurance Activities. The sectors with the highest pay gap between males

and females included Activities of extraterritorial organization and bodies, Mining and quarrying,

and Manufacturing. The lowest pay gap was found to be in Education, Human health and social

work activities and Construction.

Table 19: Average Monthly Income, by Job Sector


Employment Sector Male

(€)

Female

(€)

Total

(€)

Activities of extraterritorial organizations and bodies 977 528 855

Mining and quarrying 498 135 485

Professional, scientific and technical activities 479 379 459

SOCIALIMPACT.COM 58




(€)

Female

(€)

Total

(€)

Electricity, gas, steam and air conditioning supply 431 284 427

Education 421 405 415

Information and communication 415 339 394

Public administration and defense; compulsory social

security 395 360 391

Financial and insurance activities 379 387 381

Human health and social work activities 387 361 370

Activities of households as employers; undifferentiated

goods- and services-producing activities of households

for own use

393 180 357

Construction 358 328 357

Administrative and support service activities 372 303 355

Water supply; sewerage, waste management and

remediation activities 350 316 349

Transportation and storage 327 287 326

Agriculture, forestry and fishing 331 162 310

Manufacturing 322 207 301

Other service activities 301 271 294

Real estate activities 306 251 293

Wholesale and retail trade; repair of motor vehicles and

motorcycles 320 230 289

Accommodation and food service activities 287 239 280

Arts, entertainment and recreation 333 114 225

Other 300 172 267

Total 353 289 340


Persons with higher levels of education reported higher incomes, excluding unpaid workers.

Those with no education or only primary level education generated monthly incomes of €206, on

average, while those with at least Upper Secondary education made more than €308. Table 20

below shows average monthly salary disaggregated by level of education and sex. The gender

pay gap holds across all levels of education, with the highes differential between males and

females was much more pronounced at extremely low and extremely high levels of education.

SOCIALIMPACT.COM 59


Table 20: Average Monthly Salary, by Level of Education and Sex



Did not finish primary school 238 139 206

Primary education 277 175 255

Lower secondary education 311 207 291

Upper secondary - general 334 225 308

Upper secondary - vocational 347 267 337

Post secondary - vocational 404 325 386

Tertiary/University 434 377 411

Post graduate/Master 504 468 492

Doctorate 1057 681 985

Total 353 289 340


The top five most lucrative fields of study were Computer Science (average monthly income of

€425), Teacher Training and Education (average monthly income of €402), Law (average monthly

income of €387) and Business (average monthly income of €382). Among the lowest earners

were those who studied Services, Social Sciences and Physical Science. When looking at age,

the data showed that the youngest age group of 15-24 year olds earned the lowest average salary

at €253, followed by the oldest group that earned an average salary of €264. Those belonging to

middle-ages earned average incomes in the range of €343 to €396, showing a positive trend with

age. For a full disaggregation of net monthly salary that includes age group, geographic

classification, region, and level and field of study, see Annex 3.

Employment duration

The average employment duration, or the number of years respondents had been working in their

current job was 11.1 years overall (10.9 years for males, and 11.5 years for females). The

maximum reported job duration was 68 years, while the median stood at 7 years.

Respondents residing in rural areas retained their jobs for, on average, 1.8 more years than

residents of urban areas (respondents in rural areas reported employment durations of 11.5 years

compared to urban respondents who reported 9.7 years of employment). This is likely due to the

prevalence of agricultural jobs in rural areas, where individuals who own land, work on their farms

their entire lives.

Time spent working

The survey included questions about the number of months, in a year, individuals worked in their

current job as well as the number of hours worked per week. Employed individuals, on average,

worked 9.7 months a year and 44.4 hours per week. The median individual worked 11 months a

SOCIALIMPACT.COM 60


year and 40 hours per week. Males worked approximately 12.8 hours per week more than females

on average, for whom the average number of weekly work hours was 35.

Additionally, as illustrated in Figure 20, the plurality of males (40.5%) and females (43.0%) spent

40-50 hours working per week. A very low percentage of both males and females reported working

80 hours and above.

Figure 20: Hours Worked per Week, by Sex

On average, urban and rural males worked a similar number of hours a week, however, urban

females reported working significantly more hours (40.7) compared to their rural counterparts

(32.9 hours).

Permanent versus Temporary Employment (temporary/permanent)

The levels of permanent versus temporary work were nearly equal among employed individuals;

51.3% identified their jobs as temporary, compared to 48.8% who identified them as permanent

with no difference between males and females. It is interesting to note that 85% of youth aged

15-24 years and 66% of 24-54-year-old individuals were employed in temporary jobs while only

26.3% of those aged 55-64 were in temporary employment. Regionally, a disproportionately high

number of individuals in Mitrovica reported being permanently employed (63.8%) while only

29.1% of individuals in Ferizaj reported being in permanent employment. Table 21 below shows

the distribution of permanent employment across the country’s seven regions.

12.7

5.8

40.6

13.3

23.1

4.5

35.9

8.6

43.2

6.4 5.4

0.6

18.8

6.5

41.3

11.5

18.4

3.5

30 And Below 30-40 40-50 50-60 60-80 80 And Above

Male Female Total

SOCIALIMPACT.COM 61


Table 21: Level of Permanent Employment (%), by Region

Region Mitrovica Gjilan Pristina Gjakova Prizren Peja Ferizaj

Level of

Permanent

Employment (%)

63.8 58.2 50.1 48.6 45.1 43.5 29.1

Armed forces occupations, technicians and professionals reported high rates of permanent

employment, while most of service and sales workers tended to be in temporary employment.

Figure 21 below displays the rate of permanent employment disaggregated by occupation.

Figure 21: Level of Permanent Employment (%), by Occupation

When viewing the level of permanent employment by sector (Table 22), the highest level of

permanent employment was in mining and quarrying (84.1%), human health and social work

activities (73.1%), education (68.4%), and public administration and defense (67.1%). The lowest

level of permanent employment was in wholesale and retail (18.1%), accommodation and food

service (18.4%) and construction (23.0%).

Table 22: Level of Permanent Employment (%), by Sector

Employment Sector

Level of

Permanent

Employment (%)

Mining and quarrying 84.1

Human health and social work activities 73.1

Education 68.4

Public administration and defence; compulsory social security 67.1

Administrative and support service activities 65.0

23.9

37.8

41.6

43.0

44.2

45.8

49.1

60.4

65.8

66.9

81.7

0 10 20 30 40 50 60 70 80 90

Services And Sales Workers

Plant And Machine Operators And Assemblers

Elementary Occupations

Craft And Related Trades Workers

Managers

Clerical Support Workers

Total

Skilled Agricultural, Forestry And Fishery Workers

Professionals

Technicians And Associate Professionals

Armed Forces Occupations

SOCIALIMPACT.COM 62


Employment Sector

Level of

Permanent

Employment (%)

Real estate activities 64.8

Electricity, gas, steam and air conditioning supply 61.7

Activities of households as employers; undifferentiated goods- and services-

producing activities of households for own use 55.1

Agriculture, forestry and fishing 53.5

Water supply; sewerage, waste management and remediation activities 52.2

Information and communication 44.9

Activities of extraterritorial organisations and bodies 42.3

Professional, scientific and technical activities 40.3

Other service activities 37.1

Manufacturing 35.9

Arts, entertainment and recreation 33.1

Transportation and storage 29.1

Financial and insurance activities 25.8

Construction 23.0

Accommodation and food service activities 18.4

Wholesale and retail trade; repair of motor vehicles and motorcycles 18.1

Other 43.4

Total 48.8


When respondents held temporary employment, they were asked to cite reasons for their

temporary employment. The majority of respondents defined their contracts as being temporary,

but subject to regular renewal, indicating that many jobs are temporary in their technical

classification, but do in fact provide stable employment. Table 23 below displays the reasons for

temporary employment.

Table 23: Reasons for Temporary Employment (%)

Reason for temporary employment (%)

Contract is temporary, but subject to regular renewal 93.9

Could not find a permanent job 4.0

Contract is covering a period of training 0.9

It is a probationary period which may lead to a permanent job 0.8

Did not want a permanent job 0.3PPPPPPPP

NS

Other 0.1PPPPPPPP

NS

Total 100.0

Note: All estimates are significant at the 5% level except those marked with PPPPPPPP

NS

SOCIALIMPACT.COM 63


Level of underemployment (full-time/part-time)

According to Eurostat, an underemployed part-time worker is defined as an individual “aged 15-

74 working part-time who would like to work additional hours and is available to do so P33F” P33FP33FP33FP33FP33FP33 FP33F

34PPPPPPPP. This

definition differentiates underemployed workers from those who hold part-time jobs voluntarily,

and instead emphasizes labor force participants who are operating below their desired level of

employment. All individuals discussed in this sub-section adhere to this definition of

underemployment.

According to survey results, 89.5% of individuals reported working full-time, while 10.5% report

working part-time with females being equally likely to hold full-time jobs as their male counterparts.

There was little variation in level of employment between urban and rural areas, between age

groups, and region; full dis-aggregations by these variables can be found in Annex 3.

When individuals did work part-time, they identified the top reason for doing so as being unable

to find a full-time job, indicating that 78.9% of those who were working part-time jobs were

underemployed. This means that there is an under-supply of full-time of jobs. Table 24 below

displays the reasons for part-time work and their associated percentages and frequencies.

Table 24: Reasons for Part-time Work (%)

Reasons for working part-time (%)

Could not find a full-time job 78.9

Undergoing school, education or training 13.8

Looking after children 4.7

Own illness or disability 0.8

Does not want a full-time job 0.6

Looking after ill / elderly / incapacitated / disabled adults 0.2PPPPPPPP

NS

Other 0.9


NS

Level of vulnerable employment (self-employed with no employees or paid family

workers

Vulnerable employment is defined as being either self-employed with no employees or being an

unpaid family worker, and the rate of vulnerable employment is calculated as the share of those

in vulnerable employment as share of total employment. Persons in vulnerable employment

comprise a significant proportion of employed persons: 34.7% of the employed persons were in

vulnerable employment, representing 46.0% of females and 30.7% of males. Vulnerable

employment was more than twice as prevalent in rural areas (39.5%) than urban areas (17.5%).

Gjakova had the highest level of vulnerable employment (56.7%), while Pristina had the lowest

34 “Glossary: Underemployed Part-Time Worker - Statistics Explained”, Eurostat, 6 Nov. 2013, ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Underemployed_part-time_worker.

SOCIALIMPACT.COM 64


(18.9%). Table 25 below shows the share of vulnerable employment by geographic classification

and sex.

Table 25: Vulnerable Employment (%), by Geographic Location and Sex

Vulnerable Employment (%)

Geographic Location Male Female Total

Urban 20 12.4 17.5

Rural 33.3 58.8 39.5

Total 30.7 46 34.7


In terms of employment sector, the highest level of vulnerable employment, unsurprisingly, was

among agriculture, forestry and fishery workers (92.6%) and activities of households as

employers (89.8%). The lowest levels of vulnerable employment were in activities of

extraterrestrial organizations, education and human health and social work activities. Figure 22

displays the level of vulnerable employment by employment sector. For further dis-aggregations

by age and education level refer to Annex 3.

Figure 22: Vulnerable Employment (%), by Employment Sector

Seasonality in Employment

The labor force survey took place between March and August, which means that the survey was

able to capture some seasonality in the employment data. Surprisingly, it was found that the rate

of employment among the working age population was 2.8 percentage points lower (and

0.0

0.3

1.7

2.1

3.8

4.8

5.0

10.0

10.2

11.3

12.6

13.7

15.9

16.8

19.1

23.2

25.8

33.0

34.2

34.8

89.8

92.6

Activities Of Extraterritorial Organisations And Bodies

Education

Human Health And Social Work Activities

Public Administration And Defence

Mining And Quarrying

Financial And Insurance Activities

Information And Communication

Electricity, Gas, Steam And Air Conditioning Supply

Accommodation And Food Service Activities

Administrative And Support Service Activities

Construction

Professional, Scientific And Technical Activities

Manufacturing

Water Supply; Sewerage, Waste Management

Real Estate Activities

Other Service Activities

Arts, Entertainment And Recreation

Other

Transportation And Storage

Wholesale And Retail Trade; Repair Of Motor…

Activities Of Households As Employers

Agriculture, Forestry And Fishing

SOCIALIMPACT.COM 65


statistically significant) in the summer months (March-May) compared to the spring months (June-

July). The difference for females was 2.4 percentage points, which was lower than the difference

for males which stood at 3 percentage points (Figure 23). When a logistic regression was run, it

was found that individuals were 0.8 times as likely to be employed in the summer compared to

spring and the estimate was statistically significant even when controlling for other variables like

sex, age, education level etc. (Annex 3). Given that the survey only captured the first two months

of summer, June and July, it is likely that the survey did not capture the full effect of seasonality

which might have taken place later on in the summer and fall months, therefore these findings are

not conclusive.

Figure 23: Employment Rate (%), by Sex and Season

Since this finding is unexpected; the research team analyzed the breakdown of employment in

each sector for both seasons (Table 26). In the summer, a higher proportion of jobs were in

agriculture and construction compared to spring, which supports the theory about a higher number

of these types of jobs becoming available in the summer. However, some jobs have a lower

proportion in the summer compared to spring, notably in accommodation and food service, human

health and social work activities, public administration and defense and manufacturing. Given that

aggregate employment decreases in the summer, individuals might be switching between sectors

in the two seasons, as opposed to more inactive or unemployed individuals becoming employed

in the summer.

59.6

25.2

42.5

56.6

22.8

39.7

Male Female Total

Spring Summer

SOCIALIMPACT.COM 66


Table 26: Employment by Sector and Season

Economic Activity Spring

(%)

Summer

(%)

Total

Jobs

(N)

Agriculture, Forestry and Fishing 19.9 23.7 2,070

Mining and Quarrying 1.2 0.6 93

Manufacturing 6.3 5.6 783

Electricity, Gas, Steam and Air Conditioning Supply 2.7 2.2 260

Water Supply; Sewerage, Waste Management and Remediation

Activities 0.9 0.8 100

Construction 12.8 15.1 1,457

Wholesale and Retail Trade; Repair of Motor Vehicles and

Motorcycles 9.4 9.3 1,248

Transportation and Storage 3.2 2.7 358

Accommodation and Food Service Activities 5.2 3.6 577

Information and Communication 1.0 1.1 146

Financial and Insurance Activities 1.3 0.9 179

Real Estate Activities 0.2 0.1 19

Professional, Scientific and Technical Activities 0.8 0.3 92

Administrative and Support Service Activities 1.5 1.1 175

Public Administration and Defense; Compulsory Social Security 5.0 4.0 596

Education 6.6 6.4 867

Human Health and Social Work Activities 3.7 2.5 433

Arts, Entertainment and Recreation 2.0 2.1 270

Other Service Activities 12.3 15.1 1,514


Services-Producing Activities of Households For Own Use 3.2 2.1 250

Activities of Extraterritorial Organizations and Bodies 0.5 0.2 49

Other 0.6 0.4 81

Total 52.0 48.0 11,617


In order to further explore the effect of seasonality, the research team viewed the number of hours

worked per week in spring compared to summer, it was found that despite the percentage of those

in employment being lower in the summer, those who were still in employment worked 2.7 hours

more per week on average in summer months compared to spring months and this difference

was statistically significant (Figure 24). The difference for males was higher at 3.7 hours,

compared to females (0.6 hours, not statistically significant). This suggests that while a slightly

fewer proportion of individuals chose to work in the summer, they spend longer hours working.

SOCIALIMPACT.COM 67


Figure 24: Hours Spent Working per Week, by Sex and Season

Actual versus Self-Declared Labor Status

The survey also asked individuals about how they would classify themselves as with regards to

their labor status. It was found that of the individuals who were employed (according to the

Eurostat definition) only 70.7% identified themselves as being in employment, while 14.1% of

them indicated themselves as being unemployed and 8.8% indicated themselves as fulfilling

domestic tasks. In other words, if employment rate was calculated based on self-declared labor

status, it would only be 29.6% compared to 41.1% (the employment rate calculated based on the

Eurostat definition). This lends credence to the fact that when asked directly about labor status, it

is often understated.

Table 27: Actual versus Self-Declared Labor Status

Self-Declared Labor Status Employed (by Eurostat

Definition)

Employed, Including Self-Employment, Unpaid Work for A Family

Business or Farm, or an Apprenticeship or Paid Traineeship, Etc. 70.7

Unemployed 14.1

Pupil, Student, Further Training, Unpaid Work Experience 4.6

In Retirement or Early Retirement 1.7

Permanently Disabled 0.2

Fulfilling Domestic Tasks 8.8

Other 0.0

46

34.7

43.1

49.7

35.3

45.9

Male Female Total

Spring Summer

SOCIALIMPACT.COM 68


Unemployment

11T11T11T11T11T11T11T11TAccording to the Eurostat definition, unemployment is defined as persons aged 15-74 years who

were not employed during the definition of employment, currently available for work, or actively

seeking work (had taken steps within the last four weeks to seek out paid employment). This

definition was utilized to guide LFTUS instrument development, and all references to unemployed

individuals refer to respondents fitting to this description. The unemployment rate is calculated as

the share of unemployed individuals among the active labor force (11T11T11T11T11T11T11T11Temployed plus unemployed 11T11T11T11T11T11T11T11T

aged 15-74 years old).

Unemployment rate

The survey findings revealed that the aggregate unemployment rate in Kosovo stood at 16.3%,

with a notable difference between males and females: 22.4% of active female respondents were

unemployed, compared to 13.6% of their male counterparts.

There was a positive association between the rate of unemployment and age of respondents. The

rate of unemployment was highest among youth, aged 15-24 years, at 29.2%, with the

unemployment rate for male youth at 22.8% while that for female youth at a striking 41.8%. Only

0.6% of the persons aged 65-74 reported being unemployed; given that this age group is not

considered to be among the working age population in Kosovo, very few individuals are

economically active, and of those who are, the likelihood of employment is high. Of the individuals

in this age group, 64% identified themselves as unpaid family workers, 22.5% as self-employed

and 13.4% as employees.

Figure 25: Unemployment Rate (%), by Age and Sex

The rate of unemployment was found to be higher in urban areas, for both males and females. In

urban areas, 21% of economically active persons were unemployed compared to 15% of

22.8

16

11.5

7.25.8

0

13.6

41.8

29.8

13.4

8.5

3 2

22.4

29.2

20.1

12.2

7.65

0.6

16.3

15-24 25-34 35-44 45-54 55-64 65-74 Total

Male Female Total

SOCIALIMPACT.COM 69


individuals residing in rural areas. The disparity between male and female unemployment rate

was higher in urban areas compared to rural areas. Table 28 shows a breakdown of

unemployment rate by geographic classification and sex.

Table 28: Unemployment Rate (%), by Geographic Location and Sex

Unemployment Rate (%)


Urban 17 27.9 21

Rural 12.7 20.5 15

Total 13.6 22.4 16.3


The rate of unemployment varied by region (Table 29). The highest unemployment rate was found

in the capital city of Pristina at 24.3%, while respondents in Gjakova reported the lowest

unemployment rate at 9.5%. In remaining five regions, the rate of unemployment was nearly

similar to the aggregate unemployment rate.

Table 29: Unemployment Rate (%), by Region

Region Pristina Gjilan Mitrovica Peja Ferizaj Prizren Gjakova

Unemployment Rate (%) 24.33 16.81 15.24 14.97 14.31 14.16 9.52


Findings show that unemployment rate was lowest for those with doctorate degrees (9.9%),

however not statistically significant. This was followed by primary education (10.1%) and lack of

primary education (12.8%), while it was higher for the intermediary education levels like post-

secondary (22.1%) and tertiary/university (20%). The unemployment rate is higher for higher

levels of education with the exception of those with post graduate or master degree. This indicates

that while there might be an oversupply of semi-skilled laborers, but to a lesser-extent of highly-

skilled laborers. Conversely, this could indicate an undersupply of semi-skilled jobs, and that there

is better availability of highly skilled jobs. It is also interesting to note that the unemployment rate

is highest for those with post- secondary vocational education (22.1%). Viewing the breakdown

of unemployment rate by both level of education and sex (Table 30), it is interesting to note that

the unemployment rate for females with higher education is higher than that for their male

counterparts and vice versa for lower levels of education and those with doctorate degrees.

Table 30: Unemployment Rate (%), by Highest Level of Education and Sex



Did not finish primary school 24.8 7.8 12.8

Primary education 16.4 5.5 10.1

Lower secondary education 14.9 13 14.2

SOCIALIMPACT.COM 70




Upper secondary - general 12.3 27.9 16.6

Upper secondary - vocational 13 35 17

Post secondary - vocational 21.3 24.8 22.1

Tertiary/University 11.3 30.8 20

Post graduate/Master 16.3 17.3 16.7

DoctorateP34 FP34FP34FP34FP34 FP34FP34FP34F

35 11.3PPPPPPPP

NS 5.4PPPPPPPP

NS 9.9PPPPPPPP

NS

Total 13.6 22.4 16.4


NS

Field of study also appears to have a correlation with unemployment in Kosovo. The highest rates

of unemployment were found among individuals who studied business (31.1%), manufacturing

and construction (28.8%), and life science (25.9%), while the lowest were found among those

who studied services (12%), general programs (12.3%), and engineering (13.2%).

Table 31: Unemployment Rate (%), by Field of Study

Field of Study Unemployment Rate

(%)

Business 31.1

Manufacturing and Construction 28.8

Life Science (Including Biology and Environmental Science) 25.9

Mathematics and Statistics 24.1

Health and Welfare 22.3

Law 22.0

Social Sciences 19.8

Humanities, Languages and Arts 19.0

Computer Science 18.7

Administrative and Clerical 18.5

Physical Science (Including Physics, Chemistry, and Earth Science) 17.5

Teacher Training and Education Science 16.8

Agriculture and Veterinary 15.6

Crafts and Trades 15.5

Engineering 13.2

General Programs 12.3

Services 12.0


35 Results for doctorates are extracted from small number of observations.

SOCIALIMPACT.COM 71


Reasons for unemployment

According to responses received from unemployed persons, the two most prominent reasons for

unemployment cited by more than half of the unemployed are: a job of limited duration ended

(31.4%), and dismissed or made redundant (24.6%).

The reasons for unemployment were revealed to be different for males and females, to some

extent. While reasons for males are predominantly related to labor market factors and regulations

(dismissal or redundancy, limited contracts, education or training etc.), females are more likely

report family responsibilities too for their reason for unemployment such as looking after children

(6.5%), family/spouse did not support work (4.6%) and pregnancy/child birth (1.7%),.

Discriminatory or unsafe working environment was mentioned by only 5.4% of the unemployed

individuals, and this estimate was not very different between males and females. Lack of

reliable/safe transportation is mentioned by 1.6% of unemployed individuals.

Table 32: Reasons for Unemployment (%), by Sex

Reason for unemployment Male

(%)

Female

(%)

Total

(%)

A job of limited duration has ended 33.9 26.2 31.4

Dismissed or made redundant 27.1 19.1 24.6

Discriminatory / unsafe work environment 5 6 5.4

Own illness or disability 5.3 1.8PPPPPPPP

NS 4.2

Education or training 3.8 4.8 4.1

Looking after children 0.1PPPPPPPP

NS 6.5 2.1

Family / spouse did not support work 0.3PPPPPPPP

NS 4.6 1.7

Lack of reliable / safe / affordable transportation 1.7 1.5PPPPPPPP

NS 1.6

Looking after ill / elderly / incapacitated / disabled adults 1.4 1PPPPPPPP

NS 1.3

Pregnancy / child birth 0 1.7 0.5

Early retirement 0.3PPPPPPPP

NS 0 0.2PPPPPPPP

NS

Normal retirement 0 0 0

Other 21.1 26.6 22.8


NS

Occupation of the unemployed

Unemployed individuals were asked about the main type of work they performed for their most

recent employer. The most common types of occupation among the unemployed persons were

services and sales workers (31.3%), followed by elementary occupations (23.6%), and craft and

related trades workers (17.4%). The least common types of occupation included skilled

agricultural, forestry and fishery workers (0.6%) and technicians and associate professionals

(1.3%).

SOCIALIMPACT.COM 72


Table 33: Occupation of the Unemployed (%), by Sex

Occupation of the unemployed Male

(%)

Female

(%)

Total

(%)

Services and Sales Workers 24.9 45.3 31.3

Elementary Occupations 28.3 13.4 23.6

Craft and Related Trades Workers 23.2 4.9 17.4

Professionals 5.3 19.2 9.7

Plant and Machine Operators and Assemblers 9.8 0.2PPPPPPPP

NS 6.8

Clerical Support Workers 1.8 12.2 5.1

Managers 4.7 3.2 4.2

Technicians and Associate Professionals 1.3PPPPPPPP

NS 1.3 1.3

Skilled Agricultural, Forestry and Fishery Workers 0.7PPPPPPPP

NS 0.3PPPPPPPP

NS 0.6


NS

Time spent searching for employment

According to survey findings the average unemployed individual indicated that they spend about

10 hours in a week searching for employment. In addition, the average unemployed individual

has been searching for employment for 18.8 months or 1.6 years. Males have been searching for

4 months longer than females, who have been searching for 16.5 months, on average. According

to Eurostat, an individual is considered to be long-term unemployed if they have been searching

for a job for more than 12 months. By that definition, about 33.6% or one-third of unemployed

individuals were found to be in long-term unemployment.

Figure 26 shows the distribution of time spent searching for employment by sex. 36.2% of those

unemployed have been searching for employment for more than one year, 28.9% have searched

for 7-12 months and 35% have undertaken employment search for less than 6 months. The share

of males having searched for more than one year is somewhat greater than the share of females

(38.8% and 32.9%, respectively).

SOCIALIMPACT.COM 73


Figure 26: Time Spent Searching for Employment (years), by Sex

Type and sector of work unemployed are searching for

Unemployed individuals were asked about the type of employment for which they were searching

and were asked to choose multiple, therefore the total does not add up to 100. The majority of

the unemployed individuals – 92.4% - indicated that they were searching for full-time employment,

12.8% indicated that they were searching for part-time employment, while only 5.8% indicated

that they were pursuing their own business. The differences between males and females were

relatively minor.

Table 34: Type of Employment (%), by Sex

Type of Employment Male

(%)

Female

(%)

Total

(%)

Self-Employment/Own Business 6.8 5.8 6.4

Full-Time Employee 93.7 90.7 92.4

Part-Time Employee 8.7 12.8 10.5

The most popular sectors that unemployed individuals were searching for were service activities

(16.8%), wholesale and retail trade; repair of motor vehicles and motorcycles (16.7%), and

construction (12.6%). The most attractive sectors for women included wholesale and retail trade

(23.7%), other service activities (21.5%) and education (12%). On the other hand, the sectors that

men were mostly interested in included construction (22.2%), other service activities (13%) and

wholesale and retail trade (11.3%). Table 35 displays sectors the unemployed are looking for a

job in disaggregated by gender.

15.8

17.8

27.6

16.4

7.5

5.4

9.5

21.7

15.1

30.4

18.8

4.83.7

5.6

18.4

16.6

28.9

17.5

6.3

4.6

7.8

Less Than 3Months

4-6 Months 7-12 Months 1-2 Years 2-3 Years 3-4 Years More Than 4Years

Male Female Total

SOCIALIMPACT.COM 74


Table 35: Desired Employment Sector (%), by Sex


(%)

Female

(%)

Total

(%)

Other Service Activities 13.0 21.5 16.8


Motorcycles 11.3 23.7 16.7

Construction 22.2 0.3 12.6

Education 3.8 12.0 7.4

Accommodation and Food Service Activities 8.8 2.9 6.2


Financial and Insurance Activities 4.2 6.8 5.3

Human Health and Social Work Activities 0.8 6.9 3.5

Administrative and Support Service Activities 2.2 4.6 3.2

Public Administration and Defense; Compulsory Social Security 3.4 3.0 3.2

Transportation and Storage 5.5 0.1 3.1

Professional, Scientific and Technical Activities 3.3 2.2 2.8

Agriculture, Forestry and Fishing 3.4 1.7 2.6

Arts, Entertainment and Recreation 1.4 1.8 1.6

Electricity, Gas, Steam and Air Conditioning Supply 2.5 0.3 1.5

Information and Communication 1.0 1.8 1.4

Water Supply; Sewerage, Waste Management and Remediation

Activities 0.7 0.0 0.4

Activities of Households as Employers 0.3 0.4 0.3

Mining and Quarrying 0.5PPPPPPPP

NS 0.0 0.3PPPPPPPP

NS

Activities of Extraterritorial Organizations and Bodies 0.2PPPPPPPP

NS 0.1 PPPPPPPP

NS 0.1

Real Estate Activities 0.1PPPPPPPP

NS 0.0 0.1PPPPPPPP

NS

Other 6.2 4.2 5.3


NS

Labor Force Participation

According to Eurostat, the labor force includes members of the economically active population –

individuals who are both employed (employees or self-employed) and unemployed, but excludes

those who are economically inactive (children, students, pensioners, etc.).P35FP35FP35FP35 FP35FP35FP35FP35 F

36PPPPPPPP As such, the labor

force participation rate is the percentage of individuals who are either working or actively seeking

work as a share of the total working age population.

36 “Glossary: Labour Force - Statistics Explained”, Eurostat, 3 Dec. 2014, http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Labour_force

SOCIALIMPACT.COM 75


Labor force participation rate

The overall labor force participation rate for individuals in the survey was 49.6%. For those who

were inactive, they were asked whether or not they have ever held a regular job and 85.4% of

inactive individuals indicated that they have never held a regular job before.

Labor force participation varied widely between males and females; 67.9% of males reported

participating in the labor force, while only 31.3% of females were either employed or actively

seeking employment. The labor force participation rate was more or less uniformly distributed

across different age groups, with the highest level of labor force participation occurring between

the ages of 25 and 54, and the lowest occurring between 15-24 and 65-74. Figure 27 below

displays the labor force participation rate disaggregated by age group and sex.

Figure 27: Labor Force Participation Rate (%), by Age and Sex

Regionally, the labor force participation rate showed variation, with Ferizaj having the lowest labor

force participation rate (43.5%) and Gjakova having the highest (54.9%). The capital city of

Pristina fell in between at 48.8%.

Table 36: Labor Force Participation Rate (%), by Region

Region Gjakova Gjilan Mitrovica Prizren Pristina Peja Ferizaj

Labor Force

Participation Rate (%) 54.9 52.3 50.7 50.0 48.8 45.8 43.5


The overall labor force participation rate did not vary at all by geographic location. Males in urban

areas reported a slightly lower labor force participation rate of 65.2% as opposed compared to

their rural counterparts (68.6%), however the larger difference was between females where 36.2%

51.4

85 8580.7

65.6

20.3

67.9

27.1

40.738

34.6

24.2

8.9

31.3

39.6

64.160.2

56.3

45.9

14.3

49.6

15-24 25-34 35-44 45-54 55-64 65-74 Total

Male Female Total

SOCIALIMPACT.COM 76


of women residing in urban areas reported being economically active as compared only 29.8% of

females residing in rural areas (Table 37).

Table 37: Labor Force Participation Rate (%), by Geographic Classification and Sex

Labor Force Participation Rate (%)


Urban 65.2 36.2 50.3

Rural 68.6 29.8 49.5

Total 67.9 31.3 49.6


The labor force participation rate showed a high level of variation when disaggregated by field

and level of education: as expected, individuals who did not complete primary school reported a

low labor force participation rate of 16.6%, while respondents with post graduate and doctoral

degrees participated in the labor force at rates of 92.3% and 85.5%, respectively. Table 38 below

shows the distribution of the labor force participation rate across levels of education among

individuals covered by the survey.

Table 38: Labor Force Participation Rate (%), by Highest Level of Education

Highest Level of Education Labor Force

Participation Rate (%)

Did Not Finish Primary School 16.6

Primary Education 24.6

Lower Secondary Education 37.2

Upper Secondary - General 50.9

Upper Secondary - Vocational 64.9

Post Secondary - Vocational 60.6

Tertiary/University 77.2

Post Graduate/Master 92.3

Doctorate 85.5

Total 49.7


In terms of field of study, academic disciplines with the highest labor force participation rates were

life sciences (77.9%), services (75.2%), and engineering (75.1%). Conversely, areas of study with

the lowest were physical science (48.5%) and general programs (53.6%). Table 39 displays the

labor force participation rate disaggregated by field of study.

SOCIALIMPACT.COM 77


Table 39: Labor Force Participation Rate (%), by Field of Study

Field of study Labor Force

Participation Rate (%)

Life science (including biology and environmental science) 77.9

Services 75.2

Engineering 75.1

Teacher training and education science 74.7

Administrative and clerical 73.2

Crafts and trades 70.8

Agriculture and veterinary 70.2

Business 70

Computer science 68.2

Manufacturing and construction 67.3

Law 65

Humanities, languages and arts 64.5

Mathematics and statistics 59.5

Social sciences 59

Health and welfare 55.6

General programs 53.6

Physical science (including physics, chemistry, and earth science) 48.5


Reasons for inactivity

Inactive individuals were those who were unemployed and had not looked for work (even for a

minor job of as little as one hour per week) or tried to establish their own business, in the past 4

weeks, at the time of the survey. The most common reasons for inactivity on an aggregate level

were respondents who were in professional education or training (26.4%), looking after children

(22.2%), believe that no work is available (18.7%) and retired (13.1%). Notably, a significant

portion of the sample of inactive persons (18.7%) were discouraged workers, citing their reasons

for inactivity as believing that there is no work available.

As anticipated, reasons for inactivity varied greatly by sex, as illustrated by Table 41 below. While

only 1.0% of males cited caring for children as their primary reason for inactivity, 30.2% of females

reported being unable to participate in the labor force due to their childcare duties. Table 40

displays the reasons for inactivity broken down by sex.

SOCIALIMPACT.COM 78


Table 40: Reasons for Inactivity (%), by Sex

Reason for Inactivity Male

(%)

Female

(%)

Total

(%)

Undergoing school, education, or training 36.6 21.7 26.4

Looking after children 1.1 32.2 22.2

Believes that no work is available 24.6 16.0 18.7

Retired 19.8 10.0 13.1

Own illness or disability 10.8 6.4 7.8

Not qualified to work 2.4 7.8 6.1

Do not want to work 1.4 2.3 2.0

Looking after ill/elderly/incapacitated/disabled adults 0.8 2.2 1.7

Awaiting results for job application or recruitment 1.2 0.5 0.7

Lack of reliable /safe/affordable transportation 0.4 0.6 0.5

Waiting for a call from a public employment office 0.2 0.2 0.2

Planning to start a business 0.2 0.1 0.1

Other 0.6 0.2 0.3


Reasons for inactivity also varied by age group, with 71.4% individuals in the 15-24 age group

and 11.6% individuals in the 25-34 age group citing their reason for inactivity as undergoing

school, education or training. Those categorized in age groups between 25 and 54 were most

likely to cite childcare as their reason for inactivity (49.7% for 25-34 age group, 49.2% for 35-44

age group, and 32.1% for 45-54 age group), while those aged 55-64 were likely to reported an

illness or disability that left them unable to work (21.7%). Finally, respondents aged 65-74 were,

unsurprisingly, the most likely to cite retirement as their reason for labor force inactivity (88.5%).

Table 41: Reason for Inactivity (%), by Age

Reason for Inactivity 15-24

(%)

25-34

(%)

35-44

(%)

45-54

(%)

55-64

(%)

65-74

(%)

Total

(%)

Undergoing school, education, or

training 71.4 11.6 0.4 0.0 0.0 0.1PPPPPPPP

NS 26.4

Looking after children 6.4 49.7 49.2 32.1 15.1 1.5 22.2

Believes that no work is available 14.8 25.1 25.5 26.4 23.7 0.5 18.7

Retired 0.1 0.3 1.4 3.5 16.8 88.5 13.1

Own illness or disability 1.9 4.3 8.0 14.9 21.7 5.6 7.8

Not qualified to work 1.4 1.8 7.4 14.1 16.5 2.6 6.1

Do not want to work 1.5 2.4 2.5 3.4 2.6 0.2 2.0

Looking after

ill/elderly/incapacitated/disabled

adults

0.4 1.6 3.8 3.8 2.1 0.6 1.7

Awaiting results for job application or

recruitment 1.1 1.4 0.7 0.5 0.1PPPPPPPP

NS 0.0 0.7

SOCIALIMPACT.COM 79


Reason for Inactivity 15-24

(%)

25-34

(%)

35-44

(%)

45-54

(%)

55-64

(%)

65-74

(%)

Total

(%)

Lack of reliable / safe / affordable

transportation 0.5 0.6 0.5 0.7PPPPPPPP

NS 0.1PPPPPPPP

NS 0.5PPPPPPPP

NS 0.5

Waiting for a call from a public

employment office 0.2 0.3 0.3 0.2PPPPPPPP

NS 0.2 0.0 0.2

Planning to start a business 0.2 0.2PPPPPPPP

NS 0.1PPPPPPPP

NS 0.1 0.1PPPPPPPP

NS 0.0 0.1

Other 0.1 0.7 0.2 0.4 0.9 0.0 0.3


NS

As with the labor force participation rate, there was little variation in reasons for inactivity between

regions or geographic classification; whether respondents resided in urban or rural areas did not

appear to be correlated with any given reason for inactivity.

Discouraged workers

Discouraged workers are economically inactive individuals who have stopped looking for work

because they believe that no work is available. Our survey revealed that discouraged workers

made up 18.7% of the inactive population, or in other words, 9.4% of the working age population;

10.9% of the females were discouraged workers compared to 7.8% of males.

Table 42 shows the percent of discouraged worked among the working age population for each

of the levels of education achieved. Those with lower and upper secondary education reported

the highest percentage of discouraged workers, between 9.5% and 10.8%, while those with

postgraduate and doctoral degrees had the lowest, between 2.1% and 3.5%, as expected.

Table 42: Discouraged Workers among working age population (%), by Highest Level of Education

Highest Level Of Education Discouraged Workers (%)




Upper Secondary – General 9.5

Upper Secondary – Vocational 9.7

Post Secondary – Vocational 6.4

Tertiary/University 6.4

Post Graduate/Master 3.5

Doctorate 2.1


For field of study, services, agriculture and veterinary, business and general programs had the

highest percentage of discouraged workers, between 10% and 12%, while those with the lowest

were teacher training and education science, health and welfare, administrative and clerical and

humanities, languages and arts, between 4% and 7% (Table 43).

SOCIALIMPACT.COM 80


Table 43: Discouraged Workers (%), by Field of Study

Field of Study Discouraged Workers (%)

Services 11.7

Agriculture and Veterinary 11.6

Business 11.2

General Programs 10.1

Computer Science 9.8

Manufacturing and Construction 9.7

Mathematics and Statistics 9.4

Engineering 9.3

Crafts and Trades 9.2

Physical Science (Including Physics, Chemistry, and Earth Science) 9.1

Social Sciences 9.0

Law 8.8

Life Science (Including Biology and Environmental Science) 8.2

Humanities, Languages and Arts 7.1

Administrative and Clerical 6.3

Health and Welfare 5.1

Teacher Training and Education Science 4.6


Entrepreneurship

As previously mentioned, the Global Entrepreneurship Monitor (GEM) is an initiative designed to

collect data on individuals’ perceptions of entrepreneurship and their involvement in

entrepreneurial activity (both actual and aspirational). It places special emphasis on the individual

as the driver of entrepreneurship; this theory of change is reflected in the entrepreneurship

module of the LFTUS, which asks individual Extended Interview respondents to identify their

entrepreneurial activities and aspirations. The GEM considers the degree of involvement in

entrepreneurial activity, identifying different types and phases of entrepreneurship. The LFTUS

adopts this comprehensive model to paint a complete picture of entrepreneurship in Kosovo; not

only are respondents asked whether they have recently started a new business, they are also

asked whether they plan to start a business in the future.

Additionally, the GEM approach defines three stages of entrepreneurship: nascent, new, and

established. These types and their associated definitions are detailed in Table 44 below.

SOCIALIMPACT.COM 81


Table 44: GEM Stages of Entrepreneurship

Business Stage Definition

Nascent P36FP36FP36 FP36FP36FP36FP36 FP36F

37

A business that is less than 4 months old, the owner has taken active steps to

launch the business in the past twelve months, however the business has not paid

salaries or wages yet

New A business that is between 4 and 42 months old and is paying salaries or wages

Established A running business that has paid salaries, wages or any other payments to the

owners for more than 42 months

Total Early-stage

Entrepreneurial

Activity (TEA)

Both Nascent and New businesses are considered to be early-stage

entrepreneurial activity

Level of entrepreneurship

Survey respondents were asked whether they have already started a business or were currently

in the process of starting one. Among the entrepreneurs, 6.1% individuals reported already having

started a business and 2.5% individuals reported as being in the process of starting a business

(Table 45), with the aggregate rate of self-reported entrepreneurship standing at 8.5%. Female

entrepreneurship was quite low, with the aggregate entrepreneurship rate for women reported at

3.2% compared to 14.1% for men. About 2.2% females reported already owning a business, and

only 0.9% reported starting a new business. Similarly, 10.0% of male individuals identified

themselves as already owning a business while 4.2% of them identified themselves as being in

the process of starting a business.

Table 45: Entrepreneurship Indicators (%), by Sex

Entrepreneurship Indicator (%)

Already Started a

Business Starting a Business

Aggregate

Entrepreneurship Rate

Male 10.0 4.2 14.1

Female 2.2 0.9 3.2

Total 6.1 2.5 8.5


37GEM 2016/2017 Global Report. Global Entrepreneurship Monitor Consortium, 2017, GEM 2016/2017 Global Report , gemconsortium.org/report/49812.

SOCIALIMPACT.COM 82


About 93% of the businesses were owned by Albanians, while the rest were owned by other

ethnicities. Serbians, the second biggest ethnic group owned only 0.9% or 7 of the total

businesses. There was relatively little variation in entrepreneurial activity by age group and

geographic classification.

The survey also captured the stage of entrepreneurship the individuals were in, based on the

GEM definitions described above. Additional questions were asked to business owners to

determine whether they were a nascent, new firm or established firm owner. According to the

survey, 3.4% of the extended interview respondents were nascent entrepreneurs, 0.4% were

owners of new firms and 1.4% were owners of established firms. Thus, the aggregate

entrepreneurship rate according to the GEM method was 5.2%. In addition, the rate of Total Early-

Stage Entrepreneurial Activity (nascent entrepreneur or owner-manager of a new business) was

reported at 3.8% and established business ownership rate at 1.4%. The only neighboring country

that GEM data was available for direct comparison was Croatia, which was collected in 2016.

Kosovo's level of entrepreneurship is low when compared to Croatia, which reported a Total Early-

Stage Entrepreneurial Activity rate P37FP37FP37FP37FP37FP37 FP37FP37F

38PPPPPPPP of 8.4% in 2016 and an established business ownership rate

of 4.2%, aggregating to an overall rate of entrepreneurship of 12.6% P38F. P38FP38FP38FP38FP38FP38FP38F

39PPPPPPPP The Gem survey was

conducted in Bosnia in 2014, and the entrepreneurship rates for Bosnia recorded in 2014 were

much higher as well, a Total Early-Stage Entrepreneurial Activity (TEA) rate of 7.4% and

established business ownership rate of 6.7%, than what this study finds for Kosovo.

There was a significant amount of gender disparity for all types of entrepreneurship, with females

owning a much lower percentage of each type of business (Table 46).

Table 46: Type of Entrepreneur (%), by Sex

Type of Entrepreneur (%)

Nascent

Entrepreneur

Owner in New Firm (Less

Than 3.5 Years Old)

Owner in Established Firm

(More Than 3.5 Years Old)

Male 6.1 0.6 2.6

Female 1.0 0.2 0.3

Total 3.4 0.4 1.4


For sector, the highest proportion of business was in the agriculture, forestry and fishing sector

(32.5%), followed by wholesale and retail trade (18.4%) and other service activities (16.3%).The

most number of female businesses were in agriculture (35.6%), other service activities (24.8%),

wholesale and retail trade (12.6%), manufacturing (10.9%) and accommodation and food services

(7.2%). While the most number of male businesses were in agriculture (31.8%), wholesale and

retail trade 919.7%), other service activities (14.3%), manufacturing (10.2%) and construction

(7.8%).

39 ibid

SOCIALIMPACT.COM 83


Table 47: Business Sectors (%), by Sex

Business Sector Male

(%)

Female

(%)

Aggregate

(%)

Agriculture, forestry and fishing 31.8 35.6 32.5

Wholesale and retail trade; repair of motor vehicles 19.7 12.6 18.4

Other service activities 14.3 24.8 16.3


Construction 7.8 0.7PPPPPPPP

NS 6.4

Accommodation and food service activities 5.2 7.2 5.6

Electricity, gas, steam and air conditioning supply 3 0 2.4

Transportation and storage 2.8 0.7PPPPPPPP

NS 2.4

Arts, entertainment and recreation 1.4 2.2PPPPPPPP

NS 1.5

Activities of households as employers 1.2PPPPPPPP

NS 2.6 1.4

Information and communication 1.1PPPPPPPP

NS 0.5PPPPPPPP

NS 1

Water supply; sewerage, waste management and remediation

activities 0.4 1.1PPPPPPPP

NS 0.5

Human health and social work activities 0.3PPPPPPPP

NS 0.5PPPPPPPP

NS 0.4

Professional, scientific and technical activities 0.2PPPPPPPP

NS 0.4PPPPPPPP

NS 0.3PPPPPPPP

NS

Real estate activities 0.3PPPPPPPP

NS 0 0.2PPPPPPPP

NS

Public administration and defence; compulsory social security 0.3PPPPPPPP

NS 0 0.2PPPPPPPP

NS

Mining and quarrying 0.1PPPPPPPP

NS 0 0.1PPPPPPPP

NS

Education 0 0.2PPPPPPPP

NS 0


NS

Opportunity vs. necessity driven entrepreneurship

Besides the prevalence of entrepreneurship activity, business owners were also asked whether

they decided to start a business because they wanted to take advantage of a business opportunity

or if they decided to do so because no other opportunities were available to them, or both. About

58.3% of business owners identified their reason for engaging in entrepreneurial activity as

wanting to take advantage of a business opportunity, indicating that the majority of entrepreneurs

started businesses voluntarily. Conversely, 13.3% of business owners identified that their

motivation for starting a business was necessity-driven and that they would prefer to do something

else for income generation, while 28.4% of them noted that their reasons were driven by both

opportunity as well as necessity.

While roughly the same percentage of males and females reported participating in entrepreneurial

activities that were explicitly driven by necessity, a higher percentage of women (40.7%) reported

SOCIALIMPACT.COM 84


a combination of both opportunity and necessity as their motives for involvement in a business

than men (25.5%). Similarly, fewer women (47.0%) reported engagement in entrepreneurship

that was explicitly opportunity-driven than their male counterparts (60.9%).

There was relatively little variation in whether entrepreneurship was driven by opportunity or

necessity across geographic classification, however, regions did seem to vary in the residents’

motivation to start a business. In Gjilan, which had the highest entrepreneurship level, only 5.1%

of the business owners identified their motivation for starting a business as necessity-driven, the

lowest of all regions (Table 48). Conversely, in Prizren which had the second highest

entrepreneurship rate, 22.2% of business owners identified that they started their business out of

necessity, the highest of all regions.

Table 48: Opportunity versus Necessity-driven Entrepreneurship (%), by Region

Region

Opportunity-driven

Entrepreneurship

(%)

Necessity-driven

Entrepreneurship

(%)

Combination of

Opportunity and

Necessity

(%)

Ferizaj 60.2 5.0 34.8

Gjakova 72.8 14.6 12.6

Gjilan 76.9 5.1 18.0

Mitrovica 55.2 19.2 25.6

Peja 60.6 4.6 34.4

Pristina 60.8 10.1 28.9

Prizren 43.4 22.2 34.4

Total 58.3 13.2 28.4


Given that 91.6% of our sample was Albanian and 3.4% was Serbian, the only comparison we

could meaningfully draw was between these two ethnicities and there was an ethnic disparity in

motivation for starting a business. Only 10.9% Albanians identified that they were in necessity-

driven entrepreneurship, in contrast with 50.9% of Serbians who identified themselves as the

same. A much higher percentage of Albanians identified that they were in purely opportunity-

driven entrepreneurship (59.2%), compared to only 34.5% of Serbians.

Share of registered businesses

According to survey responses, only 52.3% of all businesses were formally registered with the

Kosovo Business Registration Agency (KBRA), while 47.7% of businesses were not. In terms of

geographic classification, 66.4% of urban business owners had registered their businesses while

only 47.7% of rural business owners had registered their businesses with KBRA. Mitrovica had

the lowest level of business registration at 40.2%, while Ferizaj (60.7%) and Pristina (57%) had

the highest. For ethnicity, the businesses registration for Albanians was high at 51.9% compared

to Serbian who only registered their business 30.8% of the times. Table 49 below shows the share

SOCIALIMPACT.COM 85


of registered businesses in each sector for the ten most popular sectors and the total number of

businesses in that sector.

Table 49: Share of Registered Businesses (%) and Total No. of Businesses (n), by Business Sector

Business Sector

Share of

Registered

Businesses

(%)

Total

Number of

Businesses

(N)

Agriculture, Forestry and Fishing 36.5 188


Motorcycles 60.1 145

Other Service Activities 41.8 107

Manufacturing 62.8 73

Construction 41.4 46

Accommodation and Food Service Activities 85.0 46

Transportation and Storage 68.9 18

Electricity, Gas, Steam and Air Conditioning Supply 77.0 16


Services-Producing Activities of Households for Own Use 71.9 15

Arts, Entertainment and Recreation 77.2 12

Total 52.3 693


Sources of start-up capital

The survey asked respondents about the main source of capital they used to start and establish

their businesses. The majority of individuals (60.8%) identified their own resources or savings as

their primary source of start-up capital, while 20.4% of the business owners took out formal loans

(from banks or microfinance organizations) in order to fund their entrepreneurial activity. An

additional 9.1% of individuals sourced funds from remittances, 7.3% from other resources

(inheritance or donations), and 3.2% from informal loan sources such as family or community

members.

While male-owned business were more reliant on personal resources and remittances, female-

owned business were more reliant on formal loans. Table 50 shows the breakdown of sources of

start-up capital by sex.

SOCIALIMPACT.COM 86


Table 50: Main Sources of Start-up Capital (%), by Sex

Main Source of Start-Up Capital (%)

Own

Resources/

Savings

Formal Loan Informal Loan Remittances Other

Male 61.1 18.7 3.2 9.7 7.3

Female 55.8 27.7 3.3 6.3 6.9

Total 60.1 20.4 3.2 9.1 7.3


There was variation by region and geographic classification in how individuals resourced their

businesses. While a higher percentage of urban business owners indicated that they sourced

their start-up capital from their own savings and formal loans than their rural counterparts, in rural

areas, informal borrowing and remittance usage were more common (Table 51).

Table 51: Main Sources of Start-up Capital (%), by Geographic Location


Own

Resources/

Savings

Formal Loan Informal Loan Remittances Other

Urban 67.4 21.9 1.3 5.2 4.3

Rural 57.8 19.9 3.8 10.3 8.2

Total 60.1 20.4 3.2 9.1 7.3


Regionally, in Pristina and Gjilan, own resources and savings were the most popular sources of

capital among all regions, while formal loans were the least popular. Remittances were an

important source of start-up capital for businesses in Gjilan and Prizen out of all regions (Table

52).

SOCIALIMPACT.COM 87


Table 52: Main Sources of Start-up Capital (%), by Region


Own Resources/

Savings Formal Loan Informal Loan Remittances Other

Ferizaj 57.0 20.3 7.9 10.2 4.6PPPPPPPP

NS

Gjakova 61.0 21.2 5.6PPPPPPPP

NS 8.5 3.7PPPPPPPP

NS

Gjilan 71.0 7.3 1.3PPPPPPPP

NS 16.9 3.5PPPPPPPP

NS

Mitrovica 65.0 22.2 1.5PPPPPPPP

NS 5.9 5.4PPPPPPPP

NS

Peja 43.9 30.3 0.2PPPPPPPP

NS 9.0 16.5

Pristina 75.2 16.7 3.9PPPPPPPP

NS 2.1PPPPPPPP

NS 2.1PPPPPPPP

NS

Prizren 54.2 21.6 2.5PPPPPPPP

NS 11.1 10.6

Total 60.1 20.4 3.2 9.1 7.3


NS

In terms of variation by age group, individuals aged 55-64 were most likely (74.7%) to fund their

businesses through their own resources and savings which is unsurprising, while members of the

youngest age bracket of 15-24 years reported a disproportionately high percentage of remittance

usage (19.7%) for new business development among all other age groups.

Some interesting variations were revealed when disaggregating by ethnicity; for instance, all of

the 7 Serbian-owned businesses sourced their start-up capital from their own savings, while

60.4% of the Albanian-owned businesses were started using own resources or savings, 23.2%

by formal or informal loans and 9.8% by remittances.

Main obstacles in growing businesses

The survey respondents were asked if they have been able to grow their business as much as

they wanted to. The majority of business owners – 65.9% - reported that they were unsatisfied

with the growth of their businesses. If individuals answered that they were unable to grow their

business to the extent they desired, they were asked about what they perceived as the main

obstacle to the growth of their business. The most commonly cited reason for lack of business

growth was lack of financial means, cited by 65% of business owners, followed by a perceived

lack of market and clients at 13.4%. Table 53 shows the distribution of the main obstacles in

business growth.

Table 53: Main Obstacles to Business Growth (%, n)

Main obstacle to business growth (%)

Lack of financial means 65.0

Lack of market and clients 13.4

High competition 9.9

Institutional barriers and lack of institutional support 3.5

Lack of working tools and professionalism 3.2

Lack of suitable workplace 2.4

SOCIALIMPACT.COM 88


High Interest Rates 1.3

Personal reasons 0.9PPPPPPPP

NS

Unfavorable climate conditions 0.5PPPPPPPP

NS


NS

By gender, about 58.6% of female business owners reported that they were not satisfied by the

growth of their business, while a higher percentage of male business owners, 67.7% identified

the same. However, there was very little variation in reasons identified as obstacles to business

expansion.

Regionally, Pristina reported a disproportionately low percentage (3.3%) of residents who

perceived a lack of market and clients to be their main obstacle to growing their businesses. This

is likely due to the fact that businesses located in or near the nation’s capital have greater access

to these resources than firms based in rural regions such as Mitrovica, where the percentage of

individuals who cited lack of market and clients as their barrier to growth was reported at 20%.

In addition, the full sample of Extended Interview respondents was also asked whether or not they

believed there will be good opportunities in the next 6 months for starting a business in the area

they live in. Only about 40.4% of the respondents replied in the affirmative, with the highest level

of optimism reported among residents of Gjilan (48.9%), Mitrovica (48.4%) and Ferizaj (48.4%)

and the lowest among residents of Gjakova (25.9%) and Peja (29.5%).

The respondents were also asked whether or not they had the knowledge, skill and experience

required to start a new business and the majority of the individuals responded in the affirmative

at 57.8%. As expected, older respondents felt more confident in their abilities to start a new

business compared to younger respondents. At the same time, residents of rural areas felt that

they were less skilled compared to their rural counterparts, with only 56.8% of the rural

respondents answering in the affirmative compared to 61.6% of urban respondents. However,

this measure is likely to be biased because of the self-reported nature of it.

Whether or not entrepreneurship is considered a desirable career choice

Survey respondents were asked whether entrepreneurship was considered a desirable career

choice in Kosovo, as well as whether it carried a high level of status and respect throughout the

country. The answer was proven to be yes, on both accounts – 97.6% of individuals overall

considered entrepreneurship to be a desirable career choice, and 93.5% considered it to carry a

high level of status and respect.

Responses stayed relatively constant regardless of sex, however, a few interesting patterns

emerged from regional disaggregations. Individuals in Gjakova were less convinced than

residents of any other region to perceive entrepreneurship as a desirable career choice (94.7%),

while residents of Pristina were least likely to report that entrepreneurship carries a high level of

respect in Kosovo (88%). Table 54 below displays perceptions of entrepreneurship in Kosovar

society, disaggregated by region.

SOCIALIMPACT.COM 89


Table 54: Perceptions of Entrepreneurship (%), by Region

Perceptions about Entrepreneurship (% that responded “Yes”)

Is entrepreneurship considered a

desirable career choice?

Does entrepreneurship carry a high level of

respect?

Ferizaj 99.0 95.4

Gjakova 94.7 89.5

Gjilan 96.6 95.7

Mitrovica 98.3 96.3

Peja 99.3 97.1

Pristina 98.5 88.0

Prizren 96.8 97.0

Total 97.6 93.5


Of the individuals who responded that they did not perceive entrepreneurship to be a desirable

career choice, the survey inquired about the reasons why they did not. The most common reason

was the prevalence of corruption and an unsafe environment with 27.2% of individuals stating

that as a reason, followed by perception of a weak economy and lack of institutional support

(31.8%) as shown in Table 55.

Table 55: Reasons for Negative Perceptions of Entrepreneurship (%, n)

Why is starting a new business not considered a desirable career choice in

Kosovo? (%)

Corruption and unsafe environment 27.2

Weak economy and lack of institutional support 18.2

No profit, lack of market 13.6

Unfavorable conditions for doing business 13.5

Lack of perspective 9.8

Lack of financial means 5.3

High competition 5.3

Fear of failure 4.8

Lack of qualification and professionalism 1.3PPPPPPPP

NS

High taxes 1.1PPPPPPPP

NS

Total 100.0


NS

Household Income

The data on household income and remittances was collected through the Extended Interview,

and there were some households in the sample where no EI could be conducted (28.8%),

therefore the analysis below is conducted on a sub-sample of the total households. Additionally,

in 30.8% of the households where two EIs were conducted (one male and one female), the

SOCIALIMPACT.COM 90


average was taken and applied to all household members. It should also be noted that in 27% of

the households, only a female EI could be conducted, while only a male EI could be conducted

in 13.1% of the households. In addition, because of the sensitivity of asking about household

income, out of the 8,604 extended interviews, 6,207 responded to the question about household

income, while the rest either did not know or declined to answer.

According to survey results for this sub-sample of households, the average annual household

income in Kosovo was €5496, with urban households earning €6230 and rural households

earning €5300, on average. Nearly half of the total households included in the survey had a total

annual household income in the range of €3,000 and €10,000, with 24.4% of households earning

in the range of €3,000 and €5,000 and 29.9% of households earning in the range of €5,000 and

€10,000, on average (Table 56). While 6% of households have annual income of less than €1,000,

12.4% of households reported annual income of €10,000 and above. The average income for the

bottom 40% was €2230, compared to 8859 for the top €60%. In addition, it was found that the

annual household income of male-headed households, at $5196, was higher than female-headed

households, at $4505 and the difference was statistically significant.

Differences are noted across regions, with Mitrovica characterized with the lowest average

income levels at €4133 and Pristina with the highest at €6487, on average. The share of

households with income of up to 1,000 Euros is highest in Mitrovica (10.3%) and lowest in Ferizaj

(3.1%). On average, 33.4% of households in Kosovo have income levels up to €3,000, with 46.6%

of households in Mitrovica belonging to this category. With regards to the income group of €10,000

and above, Mitrovica again stands worse off since only 6.8% of households reported falling into

this category, with Pristina having the highest percentage of households - 19.3% - belonging to

this income level.

Table 56: Annual Household Income (%), by Region

Annual Household income (%)

Region Average

(€) <1000

1000-

2000

2000-

3000 3000-5000

5000-

10,000 >10,000

Ferizaj 6,085.6 3.1 12.0 14.3 25.8 31.5 13.3

Gjakova 5,642.2 3.3 11.2 17.7 23.7 29.4 14.7

Gjilan 5,482.5 6.6 8.3 14.8 28.1 29.8 12.4

Mitrovica 4,133.1 10.3 20.5 15.8 21.7 24.9 6.8

Peja 5,480.5 4.7 12.0 13.6 23.6 37.1 9.0

Pristina 6,486.8 6.0 9.3 13.6 21.7 30.2 19.3

Prizren 5,296.8 6.6 10.2 16.0 27.7 28.8 10.7

Total 5,496.9 6.0 12.1 15.2 24.4 29.9 12.4


Looking at variation in employment by income, when a logistic regression was run controlling for

other variables, it was found that individuals living in households with incomes in the bottom 40%

were 0.6 times less likely to be employed compared to the top 60%. In the same way youth

belonging to the bottom 40% households were 0.8 times less likely to be employed than youth

SOCIALIMPACT.COM 91


belonging to the top 60%. There were no significant differences found for female employment

(Annex 3).

Foreign remittances as share of total income

Survey results show that 20.2% of households received remittances from outside of Kosovo.

Remittance incidence is higher in rural areas, 21.4% of households, compared to urban areas

(16.1%). Across regions, the largest share of households receiving remittances is in Gjakova

(37.1%), while the smallest share is found in Pristina, with only 11.7% of households having

received remittances.

Among remittance recipients, on average, remittances accounted for 47.7% of total household

income, of a larger share in rural areas of 48.4%, compared to urban areas (44.6%). The median

remittance receiving household received 40% remittances as a share of their household income,

with the median for urban households at 30% and for rural households at 40%. By regions, the

greatest contribution of remittances to household income was noted in Prizren, as remittances

composed 57.6% of total household income as opposed to Pristina, where remittances

contributed only 36.6% to household income. Table 57 displays remittance incidence and share

of remittances in household income among remittance receiving households disaggregated by

region.

Table 57: Remittance Incidence (%) and Share of Remittances (%), by Region

Ferizaj Gjakova Gjilan Mitrovica Peja Pristina Prizren Total

Remittance

Incidence (%) 17.0 37.1 18.5 14.5 23.5 11.7 22.4 20.2

Share of remittances

in Household Income

(%)

48.9 45.0 51.9 44.3 47.2 36.6 57.6 47.7


Foreign Remittance Use

In line with previous evidence, remittances in Kosovo are vastly used to cover consumption needs

(61.4%) with a significant small share being utilized to starting or supporting existing businesses

(0.2% and 2%, respectively). In total, 16% of remittance recipients reported spending them on

home related expenses (9.1% for construction/improvement and 6.9% for purchase of durable

goods). Only 4.7% of remittance receivers reported spending on education related expenses.

There were no major differences noted in spending patterns between urban and rural areas (Table

58).

SOCIALIMPACT.COM 92


Table 58: Remittance Usage (Purpose) by Geographic Location

Purpose remittances are used for Urban

(%)

Rural

(%)

Total

(%)

Buy consumable goods 63.2 61.0 61.4

Home construction / improvement 6.8 9.5 9.1

Pay medical expenses or medical debt 7.3 8.2 8.0

Buy durable goods 5.8 7.1 6.9

Pay education expenses 4.2 4.7 4.7

Utilities 4.9 3.1 3.4

Pay other (non-medical) debt 2.8 2.0 2.1

Support existing business 1.6 2.1 2.0

Give to family member 0.0 0.3PPPPPPPP

NS 0.2PPPPPPPP

NS

Start a new business 0.5PPPPPPPP

NS 0.1PPPPPPPP

NS 0.2

Other 3.0 1.8 2.0


NS

Remittances and Labor Market Outcomes

According to findings, the difference between the employment rate of remittance recipients and

non-remittance recipients of 1.9 percentage points is not statistically significant, however, the

labor force participation rate does show a significant difference of about 2.5 percentage points,

by remittance recipient.

Table 59: Employment Rate (%) and Labor Force Participation Rate (%), by Remittance Usage

Key Labor Marker Indicator (%)

Remittance Incidence Employment Rate Labor Force Participation

Rate

Non-remittance recipients 42.3 51.0

Remittance recipients 40.4 48.6

Difference 1.9 PPPPPPPP

NS 2.5


NS

This trend is more pronounced when looking at labor force participation rate by the level of

remittances received. Figure 28 shows the participation rates for individuals belonging to

households in each category and the respective differences in their participation rates compared

to participation rates of individuals belonging to non-remittance receiving households.

SOCIALIMPACT.COM 93


Figure 28: Labor Force Participation Rate by Level of Remittances Received

While no significant differences were found for any of the middle levels of remittances, labor force

participation rates for those receiving remittances between 80-100 percent of their household

income were 9.6 percentage points lower (and this difference was statistically significant)

compared to individuals belonging to non-remittance receiving households. This indicates that

remittances only influence individuals’ labor market outcomes only at very high level of

remittances.

Ability to work abroad and Labor Market Outcomes

In order to measure an individual’s ability to work abroad, extended interview respondents were

asked whether or not they plan to work abroad in the next 12 months. About 15.9% of respondents

indicated that they plan to work abroad with, with almost twice the proportion of males (20.2%)

indicating their intention to work abroad compared to only 11.6% of females. The proportion of

those planning to work abroad were equal between urban and rural residents.

Table 60 displays the labor market outcomes (employment, unemployment and activity rates) by

an individual’s intention/ability to work abroad. Employment rates are 5.3 percentage points higher

and activity rates are 18.6 percentage points higher for those who say that they plan to work

abroad compared to those who don’t.

Table 60: Labor Market Outcomes (%), by ability to work abroad

In the Next 12 Months, Do You Plan To Work

Outside Of Kosovo?

Employment

Rate (%)

Unemployment

Rate (%)

Activity

Rate (%)

No 42.4 11.0 48.0

Yes 47.7 26.4 66.6

Difference 5.3 15.5 18.6

Note: All estimates and differences are significant at the 5% level

51.0% 52.0% 52.1% 47.2% 51.5% 48.6% 41.5%

1.0% 1.1% -3.9% 0.5% -2.4% -9.6%

NoRemittances

< 10 10-20 20-40 40-60 60-80 80-100

*

SOCIALIMPACT.COM 94


This could either indicate that those who intend to or have the ability to work abroad are also more

likely to be active, however a more likely explanation is probably that only individuals who are

active would plan to work in another country. These findings indicate that there is a correlation,

but don’t shed light on causation, therefore the interpretation in inconclusive. What is interesting

is that unemployment rates for those who say they plan to work abroad are 2.4 times higher

compared to those who don’t. This could indicate those who have not been able to find

employment in Kosovo, are more likely to want to look for employment abroad.

Time Use

In order to ensure high data quality and increase international comparability in the collection of

time use statistics, Eurostat released a set of methodological time use research design guidelines

known as the Harmonized European Time Use approach. The Eurostat methodology has been

applied in a variety of European countries and has been established as one of the most

comprehensive sets of best practices in conducting time use research.

Time use studies are subject to a number of challenges, including recall bias and decisions

surrounding the handling of respondent multi-tasking. To minimize these issues, the harmonized

Eurostat approach recommends using a real-time, self-administered diary whereby respondents

record activities in 10-minute intervals for two randomly designated days (one weekday and one

weekend day). Respondents record their activities in their own words, and the process of

randomly selecting a week day and weekend day to record time use takes place over the span of

a year, preferably including potentially “problematic” days (national or religious holidays).

Respondents also record if other persons are present and the location where their activities take

place

While the Eurostat harmonized approach has been proven to produce reliable data that is

comparable across countries, it is lengthy and expensive, and in research contexts where time

and budget are limited, a retrospective stylized or “light” diary can offer an attractive alternative.

Given the relatively limited scope and period of performance of the MCC Kosovo LFTUS, Social

Impact has designed a time use instrument with a stylized diary, which deviates from the Eurostat

method in terms of the time period it records, but adheres to its standards of high data quality in

various ways. While the SI time use module collects data on the most recent 24-hour period

instead of on the two randomly selected days suggested by Eurostat, it borrows from the Eurostat

method in its scope by defining the resident population as individuals living in private households

in Kosovo. The SI module also applies the Eurostat coding strategy, defining a list of

predetermined codes and recording both primary and secondary activities; both methods also

record if other persons were present during the completion of the time use diary. While adhering

to the recorded time period established by Eurostat leads to more detailed entries and captures

seasonal variations in activities, the SI method is an adaptation of the Eurostat method that allows

for increased efficiency and feasibility in the research context of the MCC Kosovo LFTUS, and

the evaluation team is confident in its ability to produce high quality time use data.

As mentioned before, the time-use diary was completed as part of the extended interview with

one randomly selected male and one randomly selected female in the household between the

SOCIALIMPACT.COM 95


ages of 18-74. The survey excluded children below the ages of 18 and full-time students in order

to reduce survey burden on those populations. Individuals above the age of 74 were excluded

from the survey because they do not belong to the working age population and therefore, were

not a population of interest for this study. In addition, response rate for male extended interviews

was lower such that 43% of the extended interviews were completed by males and 57% by

females. Therefore, the analysis done on the extended interview was weighted such that the

estimates represent the true proportion of males and females in the population which is 50-50.

For a more detailed discussion on the weighting strategy, refer to Annex 2.

Types of Activities

The study found that, on an aggregate level in a given 24-hour period, individuals spent 10.8

hours sleeping and performing personal care (bathing, dressing, etc.). Overall, individuals spent

2.0 hours working, and due to the low number of students represented in the sample, 0.0 hours

studying. On average, 5.2 hours were spent conducting household and family care, indicating that

domestic duties and child care require a significant time investment from respondents. Individuals

spent an additional 1.5 hours on mass media consumption, 3.2 hours on social life and

entertainment, and 0.4 hours on sports and outdoor activities. Fewer hours were dedicated to

additional activities such as hobbies and computing (0.0 hours), voluntary work and meetings (0.2

hours), travel (0.5 hours, including commuting to and from work), and unspecified leisure time

(0.1 hours). Table 61 below displays the average number of hours spent on each activity

disaggregated by sex, geographic region, employment status, and activity status, and each

disaggregation is discussed in greater detail below. Additional tables with more detailed activities

are included in Annex 3.

Table 61: Average Time Spent on Each Activity (hours), by Sex, Geographic Location, Employment

Status, and Labor Force Status

Average Time Spent on Each Activity (Hours)

Sex Geographic

Location Employment Status

Labor Force

Status Total

Observations 3,690

4,878

3,294

5,274

3,586

597

4,309

4,183

8,568

Activity Male Female Urban Rural Employed Un-

employed Active Inactive Total

Sleeping and

Personal Care 10.6 11.0 10.7 10.9 10.2 11.3 10.3 11.3 10.8

Employment 3.7 0.8 2.4 1.8 4.8 0.3 4.1 0.0 2.0

Studying 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0

Household and

Family Care 2.5 7.2 4.3 5.7 3.7 4.9 3.9 6.5 5.2

Voluntary

Work and

Meetings

0.3 0.1 0.2 0.2 0.1 0.4 0.2 0.2 0.2

Social Life and

Entertainment 3.5 3.0 3.4 3.2 2.8 3.9 3.0 3.5 3.2

SOCIALIMPACT.COM 96


Average Time Spent on Each Activity (Hours)

Sex Geographic


Labor Force

Status Total

Sports and

Outdoor

Activities

0.6 0.2 0.5 0.3 0.3 0.5 0.3 0.4 0.4

Hobbies and

Computing 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0

Mass Media 2.0 1.2 1.7 1.4 1.4 1.8 1.5 1.6 1.5

Travel 0.7 0.3 0.6 0.4 0.7 0.6 0.7 0.4 0.5

Other or

Unspecified

Leisure Time

0.1 0.0 0.1 0.1 0.0 0.1 0.0 0.1 0.1


NS

As expected, the data show substantial variation in time use by sex. Time spent on sleep and

personal care was relatively equal between men and women, with males spending an average of

10.6 hours on sleep and grooming and females spending an average of 11 hours. In terms of

employment, males spent 3.7 hours a day working, compared to 0.8 hours a day spent by women.

The starkest differential in time spent on activities by sex was on household and family care:

women were 3.1 times more likely than men to spend time on household and family care, with

males dedicating an average of 2.5 hours per day to cleaning and child care compared to women

spending 7.2 hours on these responsibilities.

In terms of additional variation by sex, men were more likely to spend time on hobbies and

computing (0.1 hours), mass media consumption (2.0 hours), and travel/transportation (0.7 hours)

than women, who spend 0.0 hours, 1.2 hours, and 0.3 hours on these activities, respectively. Men

also spend more time on voluntary work and meetings, social life and entertainment, and sports

and outdoor activities.

To a less dramatic extent, the data show variation in time spent on activities by geographic

location. Residents of urban and rural areas spent roughly the same amount of time on

employment, on average: 2.5 hours per day versus 2.0 hours per day. Rural residents spent an

average of 5.6 hours on household and family care, compared to urban residents who spent 4.2

hours per day on these activities. All other activities showed little variation across geographic

area.

The difference in time use by employment status operated intuitively, with employed individuals

spending an average of 4.8 hours per day working, compared to 0.3 hours spent by unemployed

individuals. While the unemployed are, by definition, not working, the coded sub-activities reveal

that unemployed individuals who indicated that they were spending their time on employment

were in fact searching and applying for jobs.

Employed individuals also appear to have less time to dedicate to sleep and personal care,

spending 1.1 fewer hours on those activities than their unemployed counterparts. Similarly,

employed individuals spend an average of 1.2 fewer hours per day on household and family care,

and 1.1 fewer hours on socializing and entertaining.

SOCIALIMPACT.COM 97


While no full-time students were surveyed as per research design, unemployed individuals

appeared to spend more time studying (0.1 hours), perhaps for personal development or informal

education, than employed individuals (0.0 hours). They also dedicate more time to hobbies and

computing, sports and outdoor activities, and mass media consumption, but fewer hours on

travel/transportation, likely due to the lack of need to commute to work.

The variation in time use by labor force participation mirrored that of employment: if an individual

was active in the labor force, they spent 4.1 working than their inactive counterparts, who did not

spend any time in employment, as expected. Active individuals spent an average of 2.6 fewer

hours per day on household and family care than inactive individuals, and 0.5 fewer hours on

socializing and entertainment. The data show a larger differential between active and inactive

individuals in terms of time spent sleeping and conducting personal care than the differential

between employed and unemployed individuals for time spent on those activities: inactive persons

spend 11.3 hours on sleep and grooming, compared to active persons who spend 10.3 hours.

Similarly to employed individuals, inactive persons spent more time than active individuals on

sports and outdoor activities and mass media consumption, but fewer hours on

travel/transportation.

In addition to comparing the number of hours spent on each activity by subgroup, it is illuminating

to explore the proportion of individuals, in each group, performing each activity during each hour

of the day. On average, the majority of employment takes place between the hours of 8:00 am

and 4:00 pm, with an average of 18% of individuals working during this time period. Household

and family care activities are more widely distributed throughout the day, with up to 42% of

individuals performing these duties between the hours of 7:00 am and 8:00 pm. The peak time

for socializing and entertaining was 3:00 pm, with 28.9% of individuals reporting performing these

activities during this time, followed by 9:00 pm (26.3% of individuals socializing and entertaining).

The majority of sleeping and personal care took place between 12:00 am and 7:00 am. Figure 29

below displays the proportion of individuals performing each activity.

SOCIALIMPACT.COM 98


Figure 29: Proportion of Individuals Performing Each Activity (Aggregate)

In terms of variation by sex, the starkest difference between male and female time use during the

day was related to percent of individuals spending in employment between the hours of 8:00 am

and 5:00 pm. While an average of 26.9% of males were working during hours of peak

employment, a mere 5.9% of females reported working during this window. Additionally, time

spent on household and family care emerged as a key activity area that varied by sex, with an

average of 19.4% of males performing these duties between the hours of 8:00 am and 5:00 pm,

compared to an average of 52.1% of females performing domestic duties during the work day.

In terms of alternative activities, men and women appear to spend similar amounts of time on

sleep and personal care and social life and entertainment. Conversely, mass media and

travel/transportation, appeared to show slightly more variation by sex, with men dedicating more

time to these activities. These patterns are illustrated in Figures 30 and 31 below, which show the

proportion of males and females performing each activity during each hour of the day.


Employment



Mass MediaTravel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day

SOCIALIMPACT.COM 99


Figure 30: Proportion of Males Performing Each Activity

Figure 31: Proportion of Females Performing Each Activity

Amount of time dedicated to each activity varied by geographic location, with a higher percentage

of rural residents working between the hours of 4:00 am and 7:00 am than their urban

counterparts. Overall, an average of 21.6% of urban individuals conducted the majority of their

employment during traditional working hours (between 8:00 am and 5:00 pm) compared to 16.3%

of rural residents. Rural respondents also reported spending more time on household and family

care than residents of urban areas, dedicating up to 45.8% of their hourly time to these

responsibilities. There was relatively little variation in time or hours spent socializing or

entertaining or on hobbies and computing by geographic location. Urban residents traveled almost

twice as much as rural respondents during peak commuting hours (for instance, 4:00 pm).


Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day


Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day

SOCIALIMPACT.COM 100


Figures 32 and 33 below display the proportion of individuals performing each activity during each

hour of the day in urban and rural areas.

Figure 32: Proportion of Urban Residents Performing Each Activity

Figure 33: Proportion of Rural Residents Performing Each Activity

Additionally, how individuals spent their time throughout the day varied by employment status,

with an average of 36% of employed individuals working between the hours of 8:00 am and 5:00

pm compared to the 2.5% of unemployed individuals who spent time “working” (searching for

employment). As expected, employed individuals have less time to dedicate to other activities,

including sleep and personal care, household and family care, social life and entertainment, and

mass media. Figures 34 and 35 below highlight the difference in time use throughout the day

between employed and unemployed individuals.


Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day


Employment



Mass Media

Travel

0%

20%

40%

60%

80%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day



Figure 34: Proportion of Employed Individuals Performing Each Activity

Figure 35: Proportion of Unemployed Individuals Performing Each Activity

In terms of activity status, labor force participants followed a pattern similar to employed

individuals, with an average of 31.2% of active individuals spending their time in employment

during the workday, compared to no time spent by inactive individuals on employment,

understandably. Inactive individuals were 14 percentage points more likely than their inactive

counterparts to perform household and family care during waking hours (7:00 am-10:00 pm).

Additional variation in time use by activity status is depicted in Figures 36 and 37 below, which

show the proportion of active and inactive individuals performing each activity throughout the day.


Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day


Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day



Figure 36: Proportion of Active Individuals Performing Each Activity

Figure 37: Proportion of Inactive Individuals Performing Each Activity

Type of People Time Spent with

In order to gain a complete picture of time use, enumerators recorded who was present during

each activity that the respondent indicated they spent time on during the day. On average,

individuals conducted activities alone for 4.6 hours, while individuals conducted activities with

children under 9 years of age present for an average of 4.5 hours, indicating that many of their

daily activities may be completed concurrently with child care. Other non-household members

were present for an average of 3.3 hours, compared to 16.2 hours alongside adult household

members. The average time spent with each type of person is presented in Table 62 below and

is disaggregated by sex, geographic location, employment status, and labor force status.


Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day

Sleeping And Personal Care Employment



Mass Media

Travel

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Time of Day



Table 62: Average Time Spent with Each Type of Person (Hours)

Average Time Spent with Each Type of Person (Hours)

Sex Geographic


Labor Force

Participation

Type of

Persons Male Female Urban Rural Employed

Un-

employed Inactive Active Total

Children 2.8 5.8 4.3 4.6 3.5 5.0 3.7 5.3 4.5

Adult

household

members

14.5 17.5 15.7 16.5 14.7 15.5 14.8 17.6 16.2

Other non-

household

members

4.7 2.2 3.9 2.9 4.9 2.9 4.6 2.1 3.3

Not

applicable 5.2 4.2 4.6 4.7 4.7 5.6 4.9 4.4 4.6


Gender Norms in Work and Society

In partnership with C-Change and FHI 360, USAID developed a Compendium of Gender Scales,

a toolkit designed to assist development practitioners in assessing gender-related attitudes and

belief and evaluating their interventions. The creators of the Compendium define a scale as “a

numerical score aggregating multiple indicators believed to reflect an underlying concept” P39F. P39FP39FP39FP39FP39FP39FP39 F

40PPPPPPPP

The toolkit includes eight scales, all of which have been tested for their ability to measure gender

attitudes and predict behaviors of interest; the scale that proved most relevant to the LFTUS

research questions was the Gender Norm Attitudes Scale (GNAS). The goal of the GNAS is to

measure egalitarian beliefs about male and female gender norms, and it emphasizes questions

concerning respondents’ beliefs in and promotion of equity for girls and women, as well as beliefs

in maintaining the rights and privileges of men.

The scale was incorporated into the gender dynamics module of the Extended Interview, and

asked respondents to rate their agreement with a series of statements related to the status of

women’s position in society and the labor market in Kosovo. Incorporating the GNAS scale

assisted the research team in capturing the data required to address research question number

six, which investigates how attitudes about women's place in family and society influence

women's participation in the labor market. By providing insight on this topic, the LFTUS can

contribute to the growing body of research concerning the relationship between societal gender

dynamics and economic growth in Europe’s developing countries.

40 Nanda, Geeta. 2011. Compendium of Gender Scales. Washington, DC: FHI 360/C-Change.



Societal Gender Norms

In most cases, less than 50% of both male and female individuals agreed with negative gender

norm statements. Some statements, such as ‘A woman should take good care of her children and

not worry about other people’s affairs’ and ‘It is more important for men to be employed in the

household than women’ were exceptions to this general pattern. Interestingly, in every instance,

more females agreed with a given negative statement than their male counterparts: for example,

while 46.7% of men agree that ‘A woman has to have a husband or sons or some other male

kinsman to protect her’, 52.6% of women believe the statement to be true. The proportion of

women who believe that ‘Women should leave politics to the men’ is nearly double that of men

who responded to the statement, indicating that negative gender norm attitudes may be more

internalized by females than they are by males.

Rural residents tended to agree with negative gender norm statements more frequently than

respondents living in urban areas, with the exception of the 73.7% of urban individuals who agree

that ‘A woman should take good care of her own children and not worry about other people’s

affairs’. The contrast between urban and rural residents was particularly stark in terms of

individuals who agree that women need male kinsman for protection (40.0% and 52.2%,

respectively).

In terms of positive gender norm statements, the majority of all respondents (regardless of sex or

geographic location) agreed with all 5 statements. Women were more likely to agree with positive

statements than males; most notably, 62.4% of women agree that ‘Daughters should be told that

an important reason not to have too many children is so they can work outside the home and earn

money’, compared to 56.0% of men. An especially high proportion of both urban and rural

residents believe that ‘Daughters should have just the same chance to work outside of homes as

sons’ (95.9% and 94.8%, respectively).

Table 63 below shows the proportion of individuals who agree with the negative and positive

statements appropriated from the Gender Norm Attitudes Scale, disaggregated by sex and

geographic location.

Table 63: Agreement with Gender Norm Statements (%), by Sex and Geographic Location

Gender Norms Statements Male

(%)

Female

(%)

Differ-

ence

(pp)

Urban

(%)

Rural

(%)

Differ-

ence

(pp)

Negative Statements

It is important that sons have more

education than daughters 7.7 10.0 -2.2

7.4 9.3 -1.9

Daughters should be sent to school

only if they are not needed to help at

home

6.4 10.7 -4.3 6.1 9.2 -3.1

The most important reason that sons

should be more educated than 12.1 17.9 -5.8 10.9 16.1 -5.2



Gender Norms Statements Male

(%)

Female

(%)

Differ-

ence

(pp)

Urban

(%)

Rural

(%)

Differ-

ence

(pp)

daughters is so they can better look

after their parents when they are older

If there is a limited amount of money

to pay for tutoring, it should be spent

on sons first

9.9 13.2 -3.4 9.8 12.0 -2.2

A woman should take good care of her

own children and not worry about

other people’s affairs

69.1 77.8 -8.7 73.7 73.5 0.3PPPPPPPP

NS

Women should leave politics to the

men 22.8 40.3 -17.5 23.9 33.7 -9.7

A woman has to have a husband or

sons or some other male kinsman to

protect her

46.7 52.6 -5.9 40.0 52.2 -12.2

The only thing a woman can really rely

on in her old age is her sons 31.9 40.2 -8.2 29.1 37.9 -8.8

A good woman never questions her

husband’s opinions, even if she is not

sure she agrees with them

42.3 47.3 -5.0 40.8 45.9 -5.1

When it is a question of children's

health, it is best to do what the father

wants

24.8 34.7 -9.9 24.2 31.3 -7.1

It's more important for men in the

household to be employed than

women

56.4 54.2 2.2 51.1 56.3 -5.2

Positive Statements

Daughters should be able to work

outside the home after they have

children if they want to

88.6 93.3 -4.7

91.7 90.8 0.9PPPPPPPP

NS

Daughters should have just the same

chance to work outside of homes as

sons

94.8 97.0 -2.3 96.2 95.8 0.4PPPPPPPP

NS

Daughters should be told that an

important reason not to have too many

children is so they can work outside

the home and earn money

56.0 62.4 -6.3 58.6 59.4 -0.9PPPPPPPP

NS

I would like my daughter to be able to

work outside the home so she can

support herself if necessary

92.4 97.5 -5.1 95.9 94.8 1.2

Daughters should have the same

inheritance rights as sons 93.5 91.3 2.2 94.5 91.8 2.6

Note: All estimates and differences are significant at the 5% level except those marked with PPPPPPPP

NS



In order to gauge differences in perceptions by region and level of education, an additive index

was constructed using negative statements, that took values between 0 and 100, depending on

the number of statements the individual agreed with. Given that all statements in the index were

negative, the higher the index value, the more negative the individual’s attitude towards gender

norms. The aggregate value of the index was 37.5 which means that, on average, individuals

agreed with 4 of the 11 negative statements; males stood at 34.6, while females stood at 40.7

which indicates that women’s attitudes towards gender norms were more negative compared to

men’s attitudes. As expected, the value of the index was higher for rural respondents at 39.2,

compared to urban respondents for whom the value stood at 34.1. Regionally, the most negative

perception was found to be in Mitrovica (45.1) and least in Ferizaj (29.8). As expected, gender

norms perceptions were least negative among individuals with higher education levels (Figure

38).

Figure 38: Gender Norm Attitudes Index, by Education Level

As far as gender norms being correlated with female labor force participation rates are concerned,

when a logistic regression was run, controlling for covariates, no economically or statistically

significant correlations were found. The regression table in Annex 3 displays the coefficients on

the male gender norms index and female gender norms index, and the co-efficient on both were

negligible.

Workplace Discrimination

Extended Interview respondents, who were employed currently or in the past, were asked whether

or not they were treated equally within the workplace by their employer(s) when compared with

members of the opposite sex that they work with. The vast majority of respondent, 96.7%, said

that they were treated equally with no significant differences between males and females. About

64% of the individuals who were not treated equally indicated that this unequal treatment has

prevented them from advancing in their career. A higher proportion of males, 67.4% indicated

54.3

44.8

41.8

33.2

33.5

33.4

27.1

27.9

20.9

Did Not Finish Primary School

Primary Education

Lower Secondary Education

Upper Secondary - General

Upper Secondary - Vocational

Post Secondary - Vocational

Tertiary

Post Graduate

Doctorate



discrimination as a barrier to growth compared to 51.4% of females. This could indicate a higher

level of internalization of discrimination by women such that they don’t consider discrimination to

be a factor. When asked about who is focused more on their career, most individuals responded

that that there were no differences (58.1%), men more than women (31.4%) and women more

than men only 10.5%. It was no surprise that there were differences in perception by gender, as

shown in Figure 39.

Figure 39: Who is More Focused on Their Career, (%), by Sex

Because of the sensitivity of the question about personally facing discrimination, recognizing that

individuals may not feel comfortable providing a candid answer, they were also asked about

whether or not they believed that men and women in Kosovo are generally treated equally in the

workplace. A lower but still high proportion of individuals, 82%, indicated that there was no

discrimination in the workplace in Kosovo, with no gender differences. This could indicate that

either there is very little discrimination in Kosovo, or the fact that negative gender norms are so

internalized that individuals don’t recognize discrimination when it happens.

In addition, when individuals were asked about whether concern about discrimination has

influenced their career decisions, only 3.1% of individuals indicated it to be a concern, and there

were no differences in response by gender (Table 64). While it’s very difficult to capture the true

level of discrimination, the response to this question does indicate that discrimination in not a

major reason for poor labor force participation rates for females in Kosovo. It should also be noted

here that when individuals were asked about the main reason for their unemployment, about 4.4%

had indicated discrimination and/or unsafe work environment as a reason with no gender

differences.

Table 64: Whether Concern About Discrimination Has Influenced Career Decisions, (%), by Sex

Has Concern About Discrimination Influenced Your Career

Decisions?

Male

(%)

Female

(%)

Total

(%)

No 96.88 96.92 96.90

Yes 3.12 3.08 3.10

14.7

6.2

10.5

31.3

31.6

31.4

54.1

62.2

58.1

Female

Male

Total

Women More Than Men Men More Than Women No Difference



In addition, the entire sample of extended interview respondents were asked whether access to

childcare (or lack thereof) has influenced their career decisions. In total, 5.7% of individuals

reported that it has, with a much higher proportion of females - 9.2% - reporting in the affirmative

compared to only 2.1% of males (Table 65). This is in line with the earlier finding that a much

higher proportion of females report childcare duties as a reason for their inactivity. While more

females indicate that lack of child care has influenced their career decisions, the overall proportion

of females indicating this as a reason is lower than expected. There is a likelihood of

underreporting as the question did not consider childcare costs and affordability of childcare. In

addition, the question asked about career decisions, as opposed to asking individuals whether

they would be more interested in seeking work or working more hours if they had access to good

and affordable childcare.

Table 65: Whether Access to Child Care has Influenced Career Decisions, (%), by Sex

Has Access to Child Care (or Lack Thereof) Influenced Your

Career Decisions?

Male

(%)

Female

(%)

Total

(%)

No 85.5 74.6 80.0

Yes 2.1 9.2 5.7

Not Applicable 12.4 16.2 14.4

The survey respondents were also asked whether access to safe, reliable and affordable transport

has influenced their career decisions. About 8.9% of the respondents reported that access to

transport influenced their career decisions. While no differences were noted between males and

females, a much higher percentage, about 10.3%, of rural respondents replied in the affirmative

compared to 3.5% of urban respondents (Table 66), which is to be expected because public

transportation systems in urban areas are more developed.

Table 66: Whether Lack of Access to Transportation has Influenced Career Decisions (%), by

Geographic Location

Has Lack of Access To Safe, Reliable, / and/Or Affordable

Transport Influenced Your Career Decisions?

Urban

(%)

Rural

(%)

Total

(%)

No 91.9 81.8 83.9

Yes 3.5 10.3 8.9

Not Applicable 4.5 8.0 7.3

Household decision making

In order to detangle who in the house has a major say in household decisions, the extended

interview respondents were asked who in the household has a major say in how much to spend

on different kinds of expenses. In all decisions, a larger proportion of males reported having a

major say except for small consumer durable where a larger proportion of females reported having

a major say (Table 67). The most substantial differences were noted in large expenditures related

to land/house purchase or sale and major home repairs, while the smallest differences were noted

in expenditures related to small consumer durables, food clothing and health. All differences were

statistically significant.



Table 67: Decision-Making Power for Household Expenditures (%), by Sex

Who has a major say in how much to spend on: Male Female Difference

Land Purchase or Sale 83.2 30.6 52.6

House Purchase or Sale 83.4 35.7 47.7

Major Home Repairs 84.4 45.3 39.1

Type of Energy 83.9 47.7 36.2

Minor Home Repairs 83.6 51.3 32.3

Large Consumer Durables 80.6 55.6 25.0

Education 81.9 57.4 24.5

Health 83.7 65.0 18.7

Clothing 82.8 64.2 18.6

Food 76.9 59.8 17.1

Small Consumer Durables 64.0 67.0 -3.0


When asked about who in the household was primarily responsible for decisions on borrowing,

only 2.1% of females reported being the sole person making decisions compared to 29.6% males

(Table 68). Similarly, only 45.9% of females reported jointly making decisions with the partner or

others, while 60% of males reported the same. In addition, the survey also asked respondents

who earned any wages or salary, about who made decisions about how most of their wage or

salary income was spent over the past year. It is interesting to note that a larger proportion of

earning females (24.9%) report being the sole decision-maker about spending their salary while

20% of males report the same. Conversely, 78.4% of males reported jointly making spending

decisions about their salary with spouse or others compared to 71% of females reporting joint

decision-making about their salary.

Table 68: Household Members with Decision-Making Power (%), by Sex

Who in your household is

primarily responsible for

decisions on borrowing?

Over the past year, who

made decisions about how

most of your wage or salary

income was spent?

Household Member Male Female Male Female

Self 29.6 2.1 20.0 24.9

Spouse/Partner 0.1 29.1 0.2 2.5

Self and Spouse/Partner Jointly 16.0 26.4 26.2 40.4

Self and Others Jointly 44.0 19.5 52.2 30.6

Spouse/ Partner and Others Jointly 0.1 9.5 0.0 0.3

Other Household Member(s) 10.2 13.4 1.3 1.4




When looking at entrepreneurship, 61.9% female entrepreneurs indicated that they had a say in

making decisions about borrowing either solely or jointly with others compared to 47.5% of female

non-entrepreneurs who indicated the same – a difference of 14.4 percentage points. On the other

hand, the 1.2 percentage point difference noted for males was not statistically significant.

Asset Ownership

In order to gauge the level of ownership of productive assets by women compared to men, the

Extended Interview respondents were asked questions about the assets that were owned by the

household and among the assets that were owned by the household, who owned most of that

asset (self, spouse, self and partner jointly, self and other jointly, spouse and others jointly and

other household members). Table 69 displays the proportion of males and females who report

“self” as owning an asset either solely or jointly with other members of the household for the

different types of assets owned by the household. It is apparent that asset ownership (or

perception of ownership) for males is substantially higher than women for all assets, and all

differences are statistically significant. The biggest differences are noted in bigger assets like farm

equipment, motorized and non-motorized vehicles, agricultural land, while the smallest

differences are noted in assets like poultry, consumer durables and computers/laptops.

Table 69: Individuals Indicating “Self” as Owning Each Type of Asset Either Solely or Jointly with

Others (%), by Sex

Would you say you own most of “X” either solely or jointly

with others: Male Female Difference

Farm equipment (mechanized) 93.5 31.2 62.3

Nonfarm business equipment 88.0 27.6 60.4

Means of transportation – motorized (motorcycle, car) 87.8 36.3 51.5

Means of transportation – non-motorized (bicycle) 61.0 17.8 43.2

Agricultural land 94.7 52.1 42.5

Non-residential structures (commercial and other) 92.2 54.9 37.3

Farm equipment (non-mechanized) 94.4 65.9 28.5

Small livestock (goats, pigs, sheep) 94.1 65.7 28.4

Other land not used for agriculture (pieces, residential or

commercial) 94.3 67.4 26.9

Fish pond or fishing equipment 84.3 62.7 21.6

Housing structures (residential) 94.5 74.5 20.0

Large livestock (oxen, cattle) 91.9 72.2 19.7

Computer or laptop 77.4 62.7 14.7

Chickens, Ducks, Turkeys, Pigeons 83.5 74.5 9.0

Large consumer durables (fridge, TV, sofa) 95.9 88.5 7.4


An additive index was constructed, that took values between 0 and 100, depending on the number

of assets types the individual owned most of, out of the total types of assets owned by the



household; the higher the index value, the more types of assets the individual owned. The

aggregate value of the index was 76.1 which means that, on average, individuals in the sample

owned most of 76% of the total types of assets owned by their household; males had much higher

asset ownership at 89.4 compared to 63.3 for females. For females who were entrepreneurs, their

asset ownership was 9.2 percentage points higher than for females who were not entrepreneurs,

however, on the other hand the 1.5 percentage point difference noted for males was not

statistically significant. Women’s low ownership of assets in households is in line with the earlier

finding that females are more likely to use formal loans to start their businesses compared to

males who are more likely to use personal resources and savings. So, the fact that asset

ownership of women entrepreneurs is higher than non-entrepreneurs could either be because

higher ownership of assets has given them the ability to start a business or because

entrepreneurial women are intrinsically more motivated and thus are able to negotiate more

ownership of the household’s assets.



COMPARISON OF FINDINGS TO OTHER

SOURCES

Comparison to Existing Kosovo Data

According to the KAS LFS, working age population is composed of individuals aged 15-64 years.

However, the MCC LFS follows the Eurostat definition of working age which is 15-74 years.

Therefore, for comparative purposes, the labor markets indicators were reconstructed using the

15-64 age range and are reported in Table 70, in addition to the indicators for the 15-74 age

range. Comparing MCC LFS labor market indicators for age group 15-64 and 15-74, the most

notable difference relates to labor market participation rate and employment rate being higher

when only 15-64 aged persons are included compared to when persons aged 15-74 are included.

Given that the official retirement age in Kosovo is 64, it is understandable that a much smaller

proportion of 64-74 age group would be economically active, thus driving down the employment

and labor force participation rates when the broader age range of 15-74 is considered. However,

even with the lower estimates with the broader age range are notable higher than the 2016 KAS

estimates.

Table 70: Comparison of LFTUS Data to Existing LFS Data

Source Labor Market Indicator (%) Male Female Total

KAS 2016 Labor Force

Survey

Working age (15-64

years)

Labor force participation rate 58.3 18.6 38.7

Employment rate 43.0 12.7 28.0

Unemployment rate 26.2 31.8 27.5

Youth unemployment rate 47.2 65.4 52.4

Vulnerable employment rate 24.1 18.8 22.9

MCC 2017 Labor Force

and Time Use Study

Working age (15-64

years)






MCC 2017 Labor Force

and Time Use Study

Working age (15-74

years)






Data reveal major differences between estimates of the two data sources, whereby a brighter

labor market situation derives from MCC 2017 Labor Force and Time Use Study estimates.



According to 2016 KAS LFS, labor force participation was 38.7% compared to 52.2% estimated

using survey data from the 2017 MCC LFTUS. Similarly, while employment rate estimated by

KAS LFS was 28%, this rate is 43.1% in the MCC LFTUS. Conversely, according to MCC LFTUS

survey findings, 33.6% of employment is vulnerable, which is higher than the share reported in

the KAS LFS (22.9%). These differences are largely coming from agricultural jobs and unpaid

family workers (who are also more likely to be working in the agricultural sector). KAS LFS reports

about 4.2% of all jobs being in agriculture, whereas the MCC LFS finds that 21.7% of all jobs are

in agriculture which is also commensurate with the share of agriculture in Kosovo’s economy

(about 20%). In addition, the KAS LFS reports about 7.9% of those employed as being unpaid

family workers, whereas this study found that about 23.0% of employed individuals are unpaid

family workers. This is not surprising as this study was designed to capture these individuals at

the margin, who are easy to classify as inactive, if the respondents are not appropriately probed

about their labor force status. Given that the MCC LFS was able to capture more of these

agricultural workers, the employment estimates are higher and so are vulnerable employment

estimates. A more detailed discussion on the differences in methodology between the KAS and

MCC LFS can be found in section 14.1.

KAS estimated the rate of unemployment at 27.5%, which is higher than the MCC LFTUS,

standing at 16.7%. According to survey findings of MCC LFTUS, youth unemployment rate in

2017 was 29.2%, which is significantly lower than the 52.4% estimated by the 2016 KAS LFS.

One striking difference is found, with regards to female activity rate, reported at 18.6% from KAS

LFS, and 33% by MCC LFTUS survey. This is an important difference in absolute terms, indicating

that one third of women are active, compared to less than one in five, reported by KAS. This

difference again is coming from the fact that women are more likely to be unpaid family workers

and thus more them were captured in the MCC LFS.

Comparison to Similar European Economies

When Kosovo’s labor market outcomes are compared to those of other European economies of

similar scale and demographics, the country emerges in the middle of the road in terms of high

unemployment and low growth rates. The most salient of comparisons drawn between Kosovo

and economies of similar size and scale occurs when the LFTUS data are juxtaposed with the

labor market outcomes of other Western Balkan counties: Albania, Bosnia and Herzegovina,

Macedonia FYR, Montenegro, and Serbia, The Western Balkan region is characterized by

persistently high unemployment and prominent informal sector activities, and Kosovo’s position

among its neighbors demonstrates the need for growth-oriented programming in the country.

In order to draw comparisons, LFTUS findings are juxtaposed with official LFS findings, for each

country as well low and middle income countries as a group (as defined by the Word Bank), for

the year 2016. While the method used by all countries is Eurostat, the definition of working age

in different countries is different, as indicated in Table 71. The GDP per capita comparisons reveal

that Kosovo has the lowest per capita economic output of any Western Balkan country.



Table 71: Comparison of LFTUS Data to LFS Data from Other Western Balkan Countries

Labor Market

Indicator (%)

Kosovo Albania

40FP40FP40FP40FP40FP40 FP40FP40F

41

Bosnia &

Herzegovina P41FP41FP41FP41 FP41FP41FP41FP41 F

42

Mace

donia P42FP42FP42FP42FP42 FP42FP42FP42F

43

Monte

negro P43FP43FP43FP43 FP43FP43FP43FP43 F

44

Serbia P44FP44FP44 FP44FP44FP44FP44 FP44F

45

LMICs P45FP45FP45FP45

FP45FP45FP45FP45F

46

Data source LFTUS

(2017)

LFS

(2016)

LFS

(2016)

LFS

(2016)

LFS

(2016)

LFS

(2016)

WB

(2016)

Working age 15-74 15-64 15+ 15+ 15-64 15-64 15+

Labor force

participation rate 49.6 66.2 43.1 56.5 54.5 65.6 63.4

Female labor force

participation rate 31.3 40.3 34.4 43.9 41.9 43.4 48.9

Employment rate 41.1 56.2 32.2 43.1 44.9 55.2 59.9

Unemployment rate 16.3 15.6 25.4 23.7 17.7 15.9 5.6

Male unemployment

rate 13.6 16.4 22.5 24.4 18.2 14.6 5.3

Female

unemployment rate 22.4 14.6 30.0 22.7 17.1 16.1 6.1

Youth

unemployment rate 29.2

28.9

(15-29) 54.3 48.2 35.9 34.9 13.3

GDP per capita

(USD) (2016) PPPPPPPP

PPPPPPP46FP46FP46FP46 FP46FP46FP46FP46 F

47 3,661 4,147 4,709 5,237 6,701 5,348 4,334

The activity rate in Kosovo is the second lowest in the region (49.6%) with Bosnia and

Herzegovina reporting the lowest rate (43.1%). However, it must be noted that the rate for Bosnia

and Herzegovina refers to persons aged 15 and above, which may imply that the rate for 15-74

years old (as used in LFTUS survey) are lower than for that of persons 15 and above, thus,

potentially placing Kosovo with the lowest activity rate in the region. What is notable is that the

female activity rate in Kosovo is the lowest among all regions, even lower than Bosnia and

Herzegovina.

Regarding the rate of employment, Kosovo and Bosnia and Herzegovina are the lowest

performers, with employment rates of 41.6% and 32.2%, respectively. Looking at unemployment

41 Labour Market 2016. Instat, 2016, www.instat.gov.al/media/385914/tregu_i_punes_2016.pdf. 42 Labour Force Survey 2016. Agency for Statistics of Bosnia and Herzegovina, 2016,Labour Force Survey 2016, www.bhas.ba/tematskibilteni/TB_ARS%202016_BS_ENG_.pdf. 43 Statistical review / State statistical office of the Republic of Macedonia, ISSN 0580-454X. Population and social statistics, ISSN 1409-8997 ; 2.4.17.02(867)) 44 Labour Force Survey 2016. Monstat, 2016, https://www.monstat.org/userfiles/file/ars/2016/ARS%20-%20Godisnje%20saopstenje,%202016.pdf 45 Labour Force Survey in the Republic of Serbia, 2016. Statistical Office of the Republic of Serbia, 2016 46 Data, World Bank, 2016, data.worldbank.org/indicator/NY.GDP.PCAP.CD. 47 GDP per Capita (Current US$).” GDP per Capita (Current US$) | Data, World Bank, 2016, data.worldbank.org/indicator/NY.GDP.PCAP.CD.



rates, Kosovo has the fourth-highest unemployment rate in the region (16.3%) and is surpassed

by Bosnia & Herzegovina (25.4%), Macedonia (23.7.0%), and Montenegro (17.7%). As in Kosovo,

the unemployment rate for women is found to be higher in Bosnia and Herzegovina and Serbia

than it is for men, while in Albania, Macedonia and Montenegro, women are slightly less likely to

be in unemployment than men.

Kosovo and Albania report relatively low youth unemployment rates (29.2% and 28.9%,

respectively – though ‘youth’ is defined as those aged 15-29 in Albania). Nearly all other Western

Balkan countries report individuals in the 15-24 age group as having unemployment rates

between 35% and 54%.

Furthermore, when viewing comparisons between Kosovo and other low and middle countries

around the world as a group, it appears that Kosovo is underperforming in all measures; only four

in ten persons in Kosovo’s working age population are employed compared to six in ten in other

low and middle-income countries. The aggregate unemployment rate for Kosovo is more than two

times higher, with female unemployment rate being more three times higher.



CONCLUSIONS

Conclusions

With 41.1% of the working age population employed at the time of the survey, Kosovo compares

poorly with other countries in the region as well as comparably-sized economies in the world. The

economic situation, however, is worse than the aggregate statistic suggests. Almost half of

Kosovo’s population is economically inactive, one in five households receives a significant amount

of its income from remittances, and the economy is largely dependent on the public sector, which

supports one out of four jobs. Involuntary underemployment is endemic, with 78.9% of part-time

workers reporting an inability to find full-time labor, and vulnerable employment (self-employed

with no employees and unpaid family workers) accounting for nearly one third of total jobs.

Furthermore, while rural and urban areas have broadly comparable employment rates at the

national level, 27.8% of employed persons in rural areas reported being unpaid family workers.

Lastly, Kosovo employment is largely characterized by low-quality jobs; 30.2% of jobs were in

what the ILO labels elementary occupations , followed by services and sales workers (13.1%),

with few technicians and associate professionals.

A high proportion of unemployed individuals, one-third, are characterized by long term

unemployment (duration of unemployment being more than 12 months), with an average

unemployment duration of 18.8 months. Overall, poor labor market conditions are most commonly

reported as reasons for unemployment, though there is a strong gendered dynamic to this

phenomenon.

Entrepreneurship is limited (8.5% participation rate) and heavily dependent on personal resources

and savings for start-up capital. Unsurprisingly, the biggest obstacle to business growth is

identified as lack of financial means, which is evident from the fact that most business are started

with personal resources.

Across all labor market metrics, female participation is much lower than that of males. Females

are half as likely to be employed, twice as likely to be inactive, one fourth as likely to be

entrepreneurs, 227% more likely to report being unpaid family workers, and, according to time

use data, invest almost five more hours a day performing household and family care than men.

Whether due to cultural or economic factors, females also wield much less decision-making power

within the household. Males dominated decision-making power over household expenditures,

were more likely to own every type of household asset, and were fourteen times more likely to be

the sole decision maker for undertaking household debt. Although youth represent a valuable

economic asset, with an employment rate of only 27.7%, their potential remains underutilized.

This is particularly the case for young females, for whom only one in six is employed.



Lessons learned

Labor force indicators estimated in this study different substantially from previous estimates,

including findings reported by KAS. The primary driver of this divergence is likely the higher rates

of agricultural and unpaid family workers captured by this study. These individuals seem to have

been classified as economically inactive in past estimates, artificially dampening employment

rates. It should also be noted that past estimates are substantially more aligned with the colloquial,

rather than economic, definition of employment. This theory was empirically tested by asking

respondents about self-declared labor status. When the employment rate was calculated using

self-declared labor status, the estimate was 10.4 percentage points lower than when calculated

using the Eurostat methodology (30.8% and 41.1%, respectively). P47FP47FP47FP47FP47 FP47FP47FP47F

48PPPPPPPP This indicates that asking

directly about employment status results in a downward bias relative to the Eurostat method, due

primarily to unpaid family workers.

Recognizing this discrepancy, the research team employed four strategies to ensuring accurate

measurement of respondent employment status. First, the LTFUS survey was programmed to

ask the Eurostat question inquiring about work or absence from work in the reference week

(regular job, occasional job for self/family, occasional job for another enterprise, agricultural job)

as four different questions. This was done to ensure that enumerators were explicitly probing

respondents about each type of work. Second, question phrasing included culturally appropriate

examples of each type of work to improve respondents’ comprehension. Third, enumerators were

trained on how to emphasize and intonate key phrases when reading questions to differentiate

between the different types of work. Fourth, speed violations feature embedded in the research

team’s electronic data collection software was also used to track and remediate enumerators

through real-time data monitoring, issuing warnings whenever they were found to be going over

labor questions too quickly. This approach differs significantly from the KAS methodology, which

asks the Eurostat employment item in a single checkbox-style question.

Beyond the employment questions, the LFTUS research team made significant efforts to adhere

to the highest standards in data collection practice. The study employed various strategies for

promoting the integrity of survey data. One of the key aspects of the approach was paying

enumerators a monthly salary as opposed to remunerating on a per-survey basis. This is

important because the latter creates adverse incentives for enumerators, promoting volume and

not quality of work. Additionally, detailed tracking protocols were put in place to ensure that

enumerators were not replacing households prematurely and that a robust proportion of the

original randomized sample was reached. For surveys administered in Serb Kosovar areas, the

survey firm employed enumerators with the appropriate ethnicity and experience to avoid non-

response due to mistrust.

Before commencing fieldwork, rigorous training was provided - over a period of 5 days, followed

by 2 days of piloting - to enumerators and their supervisors on survey tools and protocols.

Enumerators were also handed physical copies of the enumerator manual during training that

48 The self-declared estimate is only 2.8 percentage points higher than the 2016 KAS estimate (30.8% and 28%, respectively).



included detailed instructions on how to administer each question and outlined good practices in

data collection. It is important to note that approximately 10 percent more trainees than required

were engaged in the training and only top performers were selected to participate in the study

based on their performance on the final quiz and during the pilot exercise. A substantial amount

of time on the first day of the training was spent informing enumerators about the importance of

the survey and explaining the Eurostat methodology. Training also emphasized ways of probing

respondents to obtain the most accurate data on employment.

Heavy enumerator oversight was ensured by experienced supervisors, such that there was one

supervisor for every 8 enumerators, with supplementary management provided by 2 fieldwork

coordinators hired directly by SI. Supervisors were required to be in the field with the enumerators

on a regular basis and occasionally conduct random, unannounced spot checks to ensure that

enumerators were showing up for fieldwork, following the correct tracking protocols, and

administering surveys properly. The data collection firm also retained back checkers or auditors

who conducted call backs on a subset of the surveys every week. Whenever a high number of

discrepancies were found between the original survey and the call back, enumerators were given

warnings and re-training three times before being relieved from work. It was observed that the

number of discrepancies went down significantly after the first few initial rounds of audits,

indicating that the enumerators became more careful after realizing that falsification of data would

lead to disciplinary action.

With regard to targeting the Head of Household as the primary respondent for the household

roster, it was revealed that these individuals were often incapacitated, or in several instances,

elderly or illiterate. Furthermore, enumerators found that in some cases, the Head of Household

was not best suited to answer questions related to employment because the cultural definition of

Head of Household in Kosovo often translates to the oldest male in the household. Therefore, the

most suited individual to complete the roster was not necessarily the Head of Household, but a

younger person who was more knowledgeable about the employment outcomes of all household

members. A pre-screening was conducted prior to the roster to identify which members were the

most knowledgeable, and at least two attempted visits were made before replacing the most

knowledgeable respondent with the next most knowledgeable respondent.

Lastly, the listing exercise that was conducted prior to data collection in sampled enumeration

areas proved to be very beneficial to enumerators when attempting to locate the sampled

households. They found that high quality sketch maps (which were produced during the listing

exercise) were crucial to successfully reaching their interview destinations. It was validated that

collecting the Head of Households’ name, address, geocoordinates and phone number during

listing was also integral to locating the correct households.



ADMINISTRATIVE

Institutional Review Board requirements and clearances

The Kosovo Labor Force and Time Use study was conducted in line with human subjects’

research guidelines both in the United States and Kosovo as well as in accordance with MCC’s

policies and procedures. The study was approved by SI’s independent and fully functional

Institutional Review Board (IRB), which has established protocols for gathering informed consent,

protecting anonymity and identifying information, and ensuring ethical data collection—including

from children and other vulnerable populations. All SI research staff completed a certified course

in Protecting Human Research Participants through the National Institutes of Health (NIH) or

Collaborative Institutional Training Initiative (CITI). SI utilized MCC’s standard template for

quantitative informed consent when collecting data.

Per the study’s design, minors were not targeted as respondents; while labor force data was

captured for all members of selected households above the age of 15, the information captured

was provided by the household head or other knowledgeable person who is over the age of 18.

In accordance with local law stipulating that minor under the age of 18 be interviewed in the

presence of an adult, randomly sampled respondents were restricted to ages 18-64.

Prior to the commencement of data collection, SI took the necessary steps to ensure that all

protocols, survey instruments and informed consent procedures to be used as part of the

research were reviewed and approved by SI’s IRB.

Data Protection, Access and Documentation plan

Data Protection

The rights and privacy of all participants who take part in data collection were respected

throughout the duration of the study, and continue to be following its conclusion. Ethics training

was given to all enumerators prior to data collection addressing issues on participants’ right to

know what the research is about; the right to choose whether to participate; the right to privacy

of responses; and the right to have no harm done to them.

All persons engaged in this contract were required to not divulge any information, whether

obtained orally or in writing from, to any unauthorized person for any purpose through non-

disclosure agreements. Enumerators were trained to keep all data and particularly identifying

information confidential. They were instructed not to discuss responses with anyone outside of

the study. Additionally, persons engaged in this study will not directly or indirectly use, or allow

the use of confidential information (including personally identifiable information) for any other

purpose other than that directly associated with the purposes of this study. In order to protect

privacy of respondents, only aggregate results are shown in this report, and only aggregate



results will be used in presentations during dissemination activities. SI is responsible for ensuring

that all public release documentation is reviewed to minimize any risk to respondent

confidentiality.

As standard practice, all data with personally identifiable information (PII) collected on paper or

electronically was saved on SurveyCTOs encrypted online servers or SI’s internal drive.

Confidential information (including PII) that was collected is only available to a small number of

approved researchers who are able to link responses to the individual who provided them. Upon

formal conclusion of the study, all PII will be destroyed.

Data Access and Documentation

All datasets submitted to MCC will be accompanied with complete documentation in the form of

standardized metadata. SI will use the MCC Evaluation Metadata Template, Nesstar, which

specifies the required metadata elements for documentation purposes. This method is compliant

with the international Data Documentation Initiative (DDI) 3 and Dublin Core Metadata Initiative

(DCMI) 4 standards, enabling compatibility with various data archiving systems.

All public use data will be fully anonymized (free of PII, including geographic identifiers and

variables that allow others to deduce the identity of individual subjects) to the extent possible. In

addition to completing the metadata template, SI will submit the anonymization package to the

MCC Disclosure Review Board (DRB) for review including the following: enumerator and trainer

manuals, questionnaires, raw data, anonymized public data, and cleaning and analysis Stata do

files.

Dissemination Plan

Dissemination of the final report will take place after the report has been officially approved by

MCC. Presentations will be made at both MCC HQ in Washington DC as well as in Kosovo for all

pertinent local stakeholders such as the KCD team, KAS, and other national or regional level

stakeholders (including women’s NGOs and organizations) for whom this study may play a crucial

role in program development. Final presentations at MCC and in Kosovo will have a strong

utilization-based focus, a guiding tenet of SI’s research work.

SI’s approach to conducting the Kosovo Labor Force and Time Use focused on use through

engagement with stakeholders, and producing timely, high quality data and reports that can be

used for decision-making. SI engaged MCC, KCD, KAS, and other stakeholders in the design and

implementation of this research to build capacity and ensure that work products are responsive to

decision-makers’ information needs. After formal submission of the final report, SI will engage

MCC, KAS, KCD, and other key stakeholders in the study with a Utilization Survey which seeks

to document how these relevant actors intend to use research findings.

Research Team Roles and Responsibilities

The SI team operated under the supervision of the MCC COR and Project Monitor. Roles and



responsibilities for the SI Research Team (RT) and HQ support are presented in Table 72, which

have been designed to leverage resources, expertise, and partnerships in a cost-efficient manner

while maintaining technical quality. The Program Manager (PM) was responsible for maintaining

ultimate technical, financial, and management responsibility for contract performance. The RT

worked closely with MCC, IDRA, and other stakeholders to ensure that the research design was

appropriate and useful.

Table 72: Roles and Responsibilities of LFTUS Research Team Members

Position Roles and Responsibilities

Program

Manager

• Oversee quality and management of entire research contract

• Primary point of contact with MCC

• Lead coordination and submission of deliverables

• Supervise Senior Survey Analyst, Senior Economic Analyst, Local Logistician,

and HQ support staff

• Oversee development of research design coordinating inputs from Senior

Survey Analyst and Senior Economic Analyst, including final research questions

(in consultation with MCC), and methodology

• Oversee development of all reports (Research Design, Listing, First 500

Surveys, Data Collection Completion, Final)

Senior Survey

Analyst

• Design survey data collection, including identifying survey methods and

sampling strategy

• Participate in initial data inventory and lead interim spot checks for data quality

• Review data and liaise with MCA-M and Trust counterparts for risk assessments

and monitoring

Senior

Economic

Analyst

• Review survey instruments

• Analyze and interpret data collected

• Confirm data collected is appropriate for answering research questions

Local

Logistician

• Coordinate evaluation logistics and field visits and supports RT with in-country

follow up

• Contribute to regular progress reports

• Liaise with local data collection partners as needed before, during, after data

collection

HQ: Manager • Assist in the review and submission of all deliverables

• Oversee data quality adherence

• Facilitate coordination with MCC and other stakeholders

• Coordinate data QA procedures

• Lead all aspects of HQ support

HQ: Program

Assistant

• Provide administrative and logistical support to the RT



REFERENCES

“Glossary: Labour Force - Statistics Explained”, Eurostat, 3 Dec. 2014,

http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Labour_force

“Glossary: Underemployed Part-Time Worker - Statistics Explained”, Eurostat, 6 Nov. 2013,

ec.europa.eu/eurostat/statistics-explained/index.php/Glossary: Underemployed_part-

time_worker.

Adugna, A., et al. (2013) ‘From Double Dip Recession to Fragile Recovery’, South East Europe

Regular Economic Report.

CIA (2016) ‘Kosovo’, The World Factbook.

16T16T16T16T16T16T16T16TEurostat. “EU Labour Force Survey.” EU Labour Force Survey - Statistics Explained, 18 Sept.

2017, ec.europa.eu/eurostat/statistics-explained/index.php/EU_labour_force_survey.

Eurostat. Harmonised European Time Use Surveys: 2008 Guidelines. Rep. no. KS-RA-08-014-

EN. Luxembourg: Office for Official Publications of the European Communities, 2009. Print.

GDP per Capita (Current US$).” GDP per Capita (Current US$) | Data, World Bank, 2017,

data.worldbank.org/indicator/NY.GDP.PCAP.CD.

GEM 2016/2017 Global Report. Global Entrepreneurship Monitor Consortium, 2017, GEM 2016/2017 Global Report, gemconsortium.org/report/49812. ILO (2016) ‘Kosovo’, Country Profiles.

Kan, Man Yee, and Stephen Pudney. Measurement Error in Stylised and Diary Data on Time

Use. Working paper no. 2007-03. Oxford: Institute for Social and Economic Research, 2007.

Print.

Kosovo Agency of Statistics. Labour Force Survey 2016. Kosovo Agency of Statistics,

2017, Labour Force Survey 2016, ask.rks-gov.net/media/3245/lfs-2016-anglisht.pdf.

16T16T16T16T16T16T16T16TLabour Force Survey 2016. Agency for Statistics of Bosnia and Herzegovina, 2016, Labour

Force Survey 2016, www.bhas.ba/tematskibilteni/TB_ARS%202016_BS_ENG_.pdf.

16T16T16T16T16T16T16T16TLabour Force Survey 2016. Monstat, 2016,

https://www.monstat.org/userfiles/file/ars/2016/ARS%20-

%20Godisnje%20saopstenje,%202016.pdf

16T16T16T16T16T16T16T16TLabour Force Survey in the Republic of Serbia, 2016. 16T16T16T16T16T16T16T16TStatistical Office of the Republic of Serbia 16T16T16T16T16T16T16T16T,

2016, http://pod2.stat.gov.rs/ObjavljenePublikacije/G2017/pdfE/G20175623.pdf

16T16T16T16T16T16T16T16TLabour Market 2016. Instat, 2016, www.instat.gov.al/media/385914/tregu_i_punes_2016.pdf.

Nanda, Geeta. 2011. Compendium of Gender Scales. Washington, DC: FHI 360/C-Change.



Nimani, S. A. (n.d.) Human Capital and the Labor Market in Kosovo. OECD Statistics Directorate. “Glossary of Statistical Terms: Employment (Eurostat Definition)”,

OECD, 29 July 2002, stats.oecd.org/glossary/detail.asp?ID=779.

Statistical review / State statistical office of the Republic of Macedonia, ISSN 0580-454X.

Population and social statistics, ISSN 1409-8997; 2.4.17.02(867))

United Nations Department of Economic and Social Affairs: Statistics Division. Guide to

Producing Statistics on Time Use: Measuring Paid and Unpaid Work. Rep. no.

ST/ESA/STAT/SER.F/93. New York: United Nations, 2004. Print

World Bank (2003) Kosovo Labor Market Study: Policy Challenges of Formal and Informal Employment. World Bank (2008) Kosovo Youth in Jeopardy: Being Young, Unemployed, and Poor in Kosovo. World Bank (2010) ‘Improving Labor Market Outcomes in Kosovo’, Unlocking Growth Potential:

Strategies, Policies, Actions – A Country Economic Memorandum.



ANNEXES

Annex 1: Data Collection Instruments

Pre-screen and Household Roster

HOUSEHOLD BACKGROUND INFORMATION Record the following information prior to approaching the household.

Variable Question Skip

Basic Information

datetime Please record today’s date (DDMMYYYY):

region_hr Please record the region where the survey takes place:

{REGION LIST}

municipality_hr Please record the municipality where the survey takes place:

{MUNICIPALITY LIST}

settlement_hr Please record the settlement where the survey takes place:

{filter:munic SETTLEMENT LIST}

ea_hr Please record the enumeration area where the survey takes place:

{filter:settlement EA LIST}

hhid_hr Please enter the sampled household’s unique identification number (Household ID):

structure_id_hr Please record the structure ID where the survey takes place:

hh_num_hr Please record the Household Number where the survey takes place

hhid_confirm Please confirm that the Household ID you selected is correct. If not, go back and select the correct one (you might need to correct the structure ID, settlement, municipality and/or region if the correct Household ID was not showing as option in the Household ID field).



hhh_name Please verify the name of the head of the household. If missing or incorrect, please re-enter:

geocode Please record the geocoordinates for this household.

PRE-SCREEN ROSTER Definitions Resident population: persons staying or intending to stay in the economic territory of Kosovo for a period of one year or more. Household: it is important to distinguish between a household UUUUUUUUdwellingUUUUUUUU, which consists of physical structure(s)/space(s) and a household UUUUUUUUunit UUUUUUUU, which consist of a single person or group of persons (related or unrelated) who share in household expenses spent on providing the household members with UUUUUUUUfood and other essential items for living UUUUUUUU. Members of the group may pool their incomes to a greater or lesser extent. Shares in household expenses include benefiting from expenses (e.g., children or persons with no income) as well as contributing to expenses. Because households are defined as people or groups of people (rather than physical structures), it is possible for more than one household to reside in a single dwelling. The extent to which a person shares in household expenses as described above will determine whether or not he/she is considered a member of the household and therefore whether or not he/she will be included in the roster for the sampled household. If a person does not contribute or benefit from any pooled expenses for food and essential items for living, the person constitutes a separate household at the same address. Household resident: a person who is considered a member of the household. Includes persons who are away but intend to return within the next year. If the person leaves and returns frequently, he/she will still be considered a member of the household if that is where he/she has economic interests (the family dwelling). Primary and secondary students who are away during the school term should consider their family home as the place of usual residence. Third-level students should consider their term-time address as their place of usual residence. Eligible persons: persons should be included in the household roster if they meet all of the following criteria:

• Are members of the resident population of Kosovo

• Are members of the sampled household (unit)

• Are of age 15 or above Primary respondent: the household roster should be completed by a person at least 18 years of age that is deemed by the household to be most knowledgeable about the labor force status of all household members. The primary respondent should complete the household roster for each eligible member of the household, including him/herself, as per the above definitions. The primary respondent may consult with other members of the household to answer the questionnaire, however UUUUUUUUno other household members should be interviewed by the study team without following procedures for obtaining informed consent UUUUUUUU. If the person considered most knowledgeable is unavailable, a similarly knowledgeable person should complete the roster in his/her absence. If no person who is knowledgeable about the household labor force status is available, a revisit should be scheduled.



Residence: the principal residence of the household is the place of the household’s center of economic interest. The center of economic interest/principal residence is not necessarily the same place as the household UUUUUUUUunit UUUUUUUU’s physical location at the time of the survey. Reference week: the past seven days, not including the day of the survey. Introduction: My name is [NAME] and I am working with IDRA, a data collection and research firm in Kosovo. We are gathering information on the workforce status in your community in order to improve projects in this and other communities. Your household has been selected to participate in this study, but before we begin I will ask a few questions about the members of this household to determine who is eligible to be interviewed. Starting from youngest to oldest, please answer the following questions for each household member of age 15 or above. Household members are defined as persons (related or unrelated) who share in household expenses spent on providing the household members with food and other essential items for living. Members of the household may pool their incomes to a greater or lesser extent. Shares in household expenses include benefiting from expenses (e.g., children or persons with no income) as well as contributing to expenses.

Variable Guidelines Skip

Basic Information

hhcounttotal Including yourself, how many people are members of this household?

hhcount Including yourself, how many people (15 years and older) are members of this household?

ps_first Please enter the household member’s first name:

ps_age What is $(ps_first)’s age in years?

<15 → end

ps_resident Is $(ps_first) a resident of Kosovo? Residents are persons staying or intending to stay in the economic territory of Kosovo for a period of one year or more.

1 Yes

0 No → end

ps_add Is $(ps_first) considered a member of any household other than this one?

1 Yes

0 No → ps_student

ps_cei Which of the following applies to $(ps_first):



[Primary and secondary students who are away during the school term should consider their family home as the place of usual residence. Third-level students should consider their term-time address as their place of usual residence.]

1 This household is his/her center of economic interest /

usual residence

2 Another household is his/her center of economic interest / → end

usual residence

ps_student Is $(ps_first) a full-time student?

1 Yes

0 No

ps_present Is $(ps_first) expected to be at this location physically in the next 24 hours? [If respondent is uncertain, select YES.]

1 Yes

0 No

ps_sex What is $(ps_first)’s sex? (select one)

1 Male

2 Female

ps_roster Do you consider $(ps_first) to be knowledgeable on the labor force status of all of your household members?

1 Yes

0 No

End

HOUSEHOLD ROSTER Consent: My name is [NAME] and I am working with IDRA, a data collection and research firm in Kosovo. We are gathering information on the workforce status in your community in order to improve projects in this and other communities. Our study is funded by the Millennium Challenge Corporation (MCC), a U.S. agency that provides assistance to other countries’ development projects. This research is being carried out by IDRA in collaboration with Social Impact, a management consulting firm based in the Washington D.C. area.



A total of 8,540 households throughout Kosovo will participate in this study, and your household is one of ten in this census enumeration area to be randomly selected. If you agree to participate, I will interview you about the labor force status of all members of your household that are of age 15 or above. This interview is expected to take around 30 minutes. Any information you provide that can identify you or other members of your household will be kept strictly confidential by the parties conducting this study, including MCC employees, employees of the survey firm, and the researchers, to the maximum extent permitted by the laws of the United States and the laws of Kosovo. The information collected will be used for statistical purposes only and will not be used for determining any sort of benefits or punish you for anything so please answer honestly. Your participation is voluntary and you may choose not to answer any or all questions for any reason. In other words, you have the alternative not to participate and there will be no consequences for nonparticipation. To compensate you for your time, you will be provided with a gift for completing the survey. More broadly, members of your community and country may benefit from this study by helping MCC understand how to best improve labor force outcomes in Kosovo. You may contact XX, the director of IDRA, at XX, if you have any questions, concerns or complaints about the study or your rights as a participant. If you have any questions, please feel free to ask me at any time. Household Background Information


Basic Information

f_rid Please enter the respondent’s unique household identification number (Respondent ID):

consent [Read consent script] Does respondent consent to participate in the survey?

1 Yes → region_t

0 No

consent_n Please specify reason for non-consent (if provided):

1 I am unable to spare the time → end

2 I do not want to spare the time → end

3 I am not interested in the topic of this survey → end

4 I am concerned about privacy → end

5 I am concerned about safety → end

6 Other (specify) → end

region_t Is this region the normal/principal residence of the household (center of economic interest)?

1 Yes → phone_r

0 No



region_a What is the normal/principal residence of the household (center of economic interest)?

{REGION LIST}

region_r Will the household return to $(region_a) in the next year (12 months)?

1 Yes

0 No

phone_r Please record the respondent’s phone number:

Household Member List

Please complete the following for each eligible member of the household:

First Name

hh_member1

hh_member2

hh_member3

…

hh_member∞

Begin Household Roster


Demographic Information

relationship A1) What is $(hh_memberX)’s relationship to the head of household? (select one)

1 Is head of household (HoH)

2 Spouse or cohabitating partner of HoH

3 Child of HoH or child of HoH spouse/partner

4 Parent of HoH or HoH spouse/partner

5 Grandparent of HoH or HoH spouse/partner

6 Grandchild of HoH or HoH spouse/partner

7 Other relative (including children-in-law)

8 Other (specify)

present A2) Is $(hh_memberX) currently living at this address?



1 Yes, he/she is currently living at this address only → birth_year

2 He/she is living at this address but is

temporarily absent

absent_r A2.a) Why is $(hh_memberX) temporarily absent?

1 Migrating for work

2 Holiday / visiting friends or family

3 In education

4 In institutional facility (hospital/prison/nursing

home/military barracks, etc.)

5 Other (specify)

absent_c A2.b) For how many months has $(hh_memberX) been living away from this address? [If less than one month, enter 1.]

absent_d A2.c) In how many months will $(hh_memberX) return to live at this address? [If less than one month, enter 1.]

birth_year A3) What is $(hh_memberX)’s year of birth? [If the respondent doesn't know, ask him/her to estimate the person's age and then calculate the estimated year of birth.]

birth_ref A3.a) When is $(hh_memberX)’s birth day? (select one) [Ask respondent for the date of birth, and then record one of the answers below. If respondent does not know the date of birth, choose one of the following based on the estimated age.]

1 Between January 1 and the end of the reference

week

2 After the end of the reference week (but before

December 31)



sex A4) What is $(hh_memberX)’s sex? (select one)

1 Male

2 Female

marital A5) What is $(hh_memberX)’s legal marital status? (select one)

1 Single

2 Married or factual relationship

3 Widowed

4 Divorced or legally separated

5 Other (specify)

ethnicity A6) What is $(hh_memberX)’s ethnicity? (select one)

1 Albanian

2 Serbian

3 Montenegrin

4 Croat

5 Turkish

6 Bosnian

7 Roma

8 Gorani

9 Ashkali

10 Egyptian

11 Other (specify)

nationality A7) Does $(hh_memberX) have Kosovo citizenship?

1 Yes → lab_status1

0 No

nationality_c A7.a) What is $(hh_memberX)’s primary country of citizenship? (select one)



{COUNTRY LIST}

Labor Force Status I will now ask some questions about $(hh_memberX)’s labor force status during the past week (the last seven days, not including today). There are many different types of work, so please listen carefully to each definition that I provide before responding. While it is possible that $(hh_memberX) has engaged in more than one type of activity, keep in mind that you must select one (and only one) "main job" from among these activities. Remember, only answer based on $(hh_memberX)’s activities in the past week.

lab_status1 B1) During the last week, has $(hh_memberX) worked in a regular job (at least one hour) for pay (in cash or in

kind) for someone who is not a member of the household, for example, a private enterprise or company, an NGO, or any other individual?

1 Yes

0 No → lab_status2

lab_status1m B1.a) Is this activity his/her MAIN job?

1 Yes

0 No

lab_status2 B2) During the last week, has $(hh_memberX) worked (at least one hour) in the non-agricultural sector on

your/their own account or in a business enterprise belonging to you/them or someone in your household (even unpaid), for example, as a trader, shop-keeper, barber, dressmaker, carpenter,

maid/domestic worker, driver, car washer, hairdresser, caterer, baby sitter etc.?

1 Yes



1 Yes

0 No

lab_status3 B3) During the last week, has $(hh_memberX) done any occasional job (at least one hour) for pay or profit that

was not for family or self, such as sold goods in the street, helped someone with his/her business, sold some homemade products, washed cars, repaired cars, etc.?



1 Yes



1 Yes

0 No

lab_status4 B4) During the last week, has $(hh_memberX) worked (at least one hour) on a farm owned or rented by

you/them or a member of your household (even unpaid), whether in cultivating crops or in other farm maintenance tasks, or cared for livestock belonging to you/them or a member of your household?

1 Yes

0 No → ROUTING

lab_status4t B4.a) Is at least part of this agricultural output intended to be sold or bartered?

1 Yes – at least part of the production is intended

to be sold or bartered

0 No – the whole of the production is only for own → lab_status5a

Consumption

lab_status4m B4.b) Is this activity his/her MAIN job?

1 Yes → job_char

0 No

lab_status5a B4.c) Does this agricultural output constitute an important contribution to the total consumption of the

household?

1 Yes

0 No → lab_status5p

lab_status5m B4.d) Is this activity his/her MAIN job?

1 Yes

0 No



lab_status5p B4.e) What percentage does output from $(hh_memberX)’s farming activities contribute to the total household consumption?

ROUTING If ANY of the above are selected as MAIN JOB (lab_statusXm=1):

→ job_char

In the previous section, you did not specify $(hh_memberX) as having worked in his/her “main job” last week. This may because he or she is not working generally, or it may be because he/she was temporarily absent from his/her main job. I will now therefore ask you some questions about labor activities which $(hh_memberX) may have been temporarily absent from for the whole of last week.

lab_status2_1 C1) During the last week, was $(hh_memberX) temporarily absent from a regular job for pay (in cash

or in kind) for someone who is not a member of the household, for example, a private enterprise or company, an NGO, or any other individual?

1 Yes

0 No → lab_status2_2

lab_status2_1m C1.a) Is this activity his/her MAIN job?

1 Yes → lab_ue_reas

0 No

lab_status2_2 C2) During the last week, was $(hh_memberX) temporarily absent from work in the non-agricultural

sector on your/their own account or in a business enterprise belonging to you/them or someone in your household (even unpaid), for example, as a trader, shop-keeper, barber, dressmaker, carpenter, maid/domestic worker, driver, car washer, hairdresser, caterer, baby sitter etc.?

1 Yes

0 No → lab_status2_4

lab_status2_2m C2.a) Is this activity his/her MAIN job?

1 Yes → lab_ue_reas

0 No

lab_status2_4 C3) During the last week, was $(hh_memberX)



temporarily absent from work on a farm owned or rented by you/them or a member of your household (even unpaid), whether in cultivating crops or in other farm maintenance tasks, or caring for livestock belonging to you/them or a member of your household?

1 Yes

0 No → ue_ever

lab_status2_4t C3.a) Is at least part of this agricultural output intended to be sold or bartered?

1 Yes – at least part of the production is intended → lab_ue_reas

to be sold or bartered

0 No – the whole of the production is only for own

consumption

lab_status2_5a C3.b) Does this agricultural output constitute an important contribution to the total consumption of the

household?

1 Yes

0 No → ue_ever

lab_status2_5p C3.c) What percentage do $(hh_memberX)’s farming activities contribute to the total household consumption?

lab_ue_reas C4) Why did $(hh_memberX) not work last week in his/her main job? [IMPORTANT! This is a core variable for the labor force survey. Please probe for an answer other than "don't know. Read each option until the right answer is arrived at.]

1 Education or training

2 Off-season → lab_ed_pay

3 Bad weather → lab_ed_pay

4 Free days due to flexible work time → lab_ed_pay

5 Annual leave → lab_ed_pay

6 Maternity leave → lab_ed_pay

7 Own illness, injury, or temporary disability → lab_ed_pay



8 Slack work for technical or economic reasons → lab_ed_pay

9 Suspension through employer, with right to return → lab_ed_pay

to the place of work (lay-off)

10 Labor dispute, strike, or lock-out → lab_ed_pay

11 Bankruptcy, closing down (closure) → lab_ed_pay

12 Looking after children → lab_ed_pay

13 Looking after ill / elderly / incapacitated / → lab_ed_pay

disabled adults

14 Lack of reliable / safe / affordable transportation → lab_ed_pay

15 Other (specify) → lab_ed_pay

lab_ed_app C5) Is this “education or training” vocational training (apprenticeship or traineeship)?

1 Yes

0 No → lab_ed_req

lab_ed_tr C5.a) Is $(hh_memberX) receiving cash or fringe benefits that are directly connected to participation in this training?

1 Yes

0 No

lab_ed_req C5.b) Which of the following apply to this “education or training” (select all):

1 Participation is required by an employer

2 Training takes place inside normal working hours

3 Training is directly connected to the current job

4 None of the above apply

lab_ed_pay C6) During this absence, is $(hh_memberX) receiving at least 50 percent of his/her wage or salary?

1 Yes

0 No



lab_ed_stat C7) In this job or business from which $(hh_memberX) was temporarily absent during the reference week, is he/she an:

1 Employee

2 Self-employed with employees

3 Self-employed without employees

4 Unpaid family worker

lab_ed_abs C8) What is the total anticipated duration of $(hh_memberX)’s absence from work (in months)? [If less than one month, enter 1.]

lab_ed_ret C9) Does he/she intend to return?

1 Yes

0 No

Characteristics of Main Job

job_char D1) In $(hh_memberX)’s main job, he/she is an:

1 Employee

2 Self-employed with employees → job_year

3 Self-employed without employees → job_year

4 Unpaid family worker → job_year

job_cont D1.a) Does he/she have a contract?

1 Yes

0 No → job_type

job_status D1.b) Is $(hh_memberX)’s job: [If the household member has a limited duration contract which is expected to be renewed, he/she should decide for him/herself whether they consider their job to be permanent or temporary.]

1 Temporary

2 Permanent → job_type

job_temp D1.c) Why is $(hh_memberX)’s contract temporary?



1 Contract is temporary, but subject to regular → job_type

renewal

2 Contract is covering a period of training → job_type

3 Could not find a permanent job → job_type

4 Did not want a permanent job

5 It is a probationary period which may lead to a → job_type

permanent job

6 Other (specify) → job_type

job_temp_dw D1.d) Why does $(hh_memberX) not want a permanent job?

job_type D1.e) Is $(hh_memberX)’s job:

1 Full-time → job_emp

2 Part-time

job_pt_r D1.f) What is the main reason $(hh_memberX) is working part-time instead of full-time?

1 Looking after children (personally and NOT as a → job_emp

job or volunteer for some agency)

2 Looking after ill / elderly / incapacitated / → job_emp

disabled adults (personally and NOT as a job or volunteer for some agency)

3 Own illness or disability → job_emp

4 Undergoing school, education or training → job_emp

5 Could not find a full-time job → job_emp

6 Does not want a full-time job

7 Other (specify) → job_emp

job_pt_dw D1.g) Why does $(hh_memberX) not want a full-time job?



job_emp D1.h) What type of employer does $(hh_memberX) have?

1 Government, public sector or security forces

2 State-owned enterprise

3 Private company or enterprise

4 International organization

5 NGO, non-profit or humanitarian organization

6 Self-employed

7 Other private individual

8 Other (specify)

job_year D2) How many years ago did he/she start his/her current job? [If less than one year, enter 0.]

>0 →

job_months_py

job_months D2.a) How many total months has he/she worked in this job? [If less than one month, enter 1.]

→ job_hours

job_months_py D2.b) In a typical year, how many months does $(hh_memberX) work in his/her main job? [If less than one month, enter 1.]

job_hours D3) How many hours per week does $(hh_memberX) usually work in his/her main job? [If respondent works a standard work day and standard work week, multiply number of hours worked per day by the number of days worked per week].

job_act_o D4) What is the main sector of $(hh_memberX)’s employer (or if self-employed, $(hh_memberX))? [Record open-ended response].



job_act D4.a) What is the main economic activity that $(hh_memberX)’s employer (or if self-employed, $(hh_memberX)) undertakes?

[INSERT NACE REV 2 CODE]

job_func_o D5) What is the main type of work that $(hh_memberX) does for his/her job (name of occupation)? [Record open-ended response].

job_func_f D5.a) What is the main type of work that $(hh_memberX) does for his/her job (name of occupation)? [First ask the respondent for an open-ended answer then select and verify the ISCO-08 code with him/her].

[INSERT ISCO-08 CODE]

job_func D5.b) Please select an occupation sub-category.

job_loc D6) Where is $(hh_memberX)’s usual or prevalent place of work in this job/activity?

{MUNICIPALITY LIST}

job_income D7) What is the net monthly pay (€) $(hh_memberX) gets from his/her main job? [Net monthly pay is the person’s monthly take home pay, net of all withholdings].

→ ed_student

Previous Work Experience of Persons Not in Employment

ue_ever E1) Has $(hh_memberX) ever been in regular employment before? (job as employee, self-employed, or unpaid family member. Purely occasional jobs or work during vacations should not be considered as previous work experience)

1 Yes

0 No → es_active

ue_year E2) How many years ago was $(hh_memberX) last in regular employment?



ue_reas E3) What is the main reason he/she stopped working?

1 Dismissed or made redundant

2 A job of limited duration has ended

3 Looking after children → ue_char

4 Looking after ill / elderly / incapacitated / → ue_char

disabled adults

5 Own illness or disability → ue_char

6 Pregnancy / child birth → ue_char

7 Discriminatory / unsafe work environment → ue_char

8 Family / spouse did not support work → ue_char

9 Education or training → ue_char

10 Early retirement → ue_char

11 Normal retirement → ue_char

12 Lack of reliable / safe / affordable transportation → ue_char

13 Other (specify)

ue_seas E4) Does $(hh_memberX) usually work for only part of the year (6 months or less)?

1 Yes

0 No → ue_char

ue_seas_d E4.a) Please describe in more detail the type of work that $(hh_memberX) usually undertakes for part of the year:

ue_seas_a E4.b) Does $(hh_memberX) go abroad for this work?

1 Yes

0 No → ue_char

ue_seas_c E4.c) To which country does $(hh_memberX) most regularly go abroad for work?



{COUNTRY LIST}

ue_char E5) In $(hh_memberX)’s most recent main job, he/she was an:

1 Employee

2 Self-employed with employees

3 Self-employed without employees

4 Unpaid family worker

ue_act_o E6) What is the main sector of $(hh_memberX)’s most recent employer (including family and/or self)? [Record open-ended response].

ue_act E6.a) What is the main economic activity that $(hh_memberX)’s most recent employer (including family and/or self) undertook?


es_func_o E7) What is the main type of work that $(hh_memberX) did for his/her last main job (name of occupation)? [Record open-ended response].

ue_func_f E7.a) What is the main type of work that $(hh_memberX) did for his/her last main job (name of occupation)?


ue_func E7.b) Please select an occupation sub-category.

Employment Search Information for Persons Not in Employment

es_active F1) In the last 4 weeks, has $(hh_memberX) looked for work (even if for a minor job of as little as one hour per week) or tried to establish his/her own business? [IMPORTANT! This is a core variable for the labor force survey. If the respondent says that they don't know, please provide some examples of activities that constitute looking for working, such as asking friends/relatives about work, reading and responding to newspaper advertisements, contacting public employment office, contacting an employment agency,



applying for jobs, taking tests or interviewing for jobs, looking for land of premises for own business, etc.].

1 Yes → es_avail

0 No

es_ia_r F2) What is the main reason $(hh_memberX) did not look for work in the last 4 weeks? (select one) [IMPORTANT! This is a core variable for the labor force survey. Please probe for an answer other than "don't know."].

1 Looking after children (personally and NOT as a → ed_student

job or volunteer for some agency)

2 Looking after ill / elderly / incapacitated / → ed_student

disabled adults (personally and NOT as a job or volunteer for some agency)

3 Own illness or disability → ed_student

4 Undergoing school, education, or training → ed_student

5 Retired → ed_student

6 Planning to start a business → ed_student

7 Believes that no work is available → ed_student

8 Awaiting results for job application or recruitment → es_avail

9 Waiting for a call from a public employment office → es_avail

10 Not qualified to work → ed_student

11 Does not want to work

12 Lack of reliable / safe / affordable transportation → ed_student

13 Other (specify) → ed_student

es_ia_dwr F2.a) Why does $(hh_memberX) not want to work? [Record open-ended response].

→ ed_student

es_avail F3) If $(hh_memberX) were to find employment now, would he/she be able and ready to start working within the next 2 weeks?



[IMPORTANT! This is a core variable for the labor force survey. Please probe for an answer other than "don't know."].

1 Yes

0 No

es_months F4) How many months has $(hh_memberX) been looking for a job or trying to establish his/her own business? [If less than one month, enter 1.]

es_hours F5) How many hours per week does $(hh_memberX) usually spend looking for a job or trying to establish his/her own business?

es_type F6) What type of employment is $(hh_memberX) pursuing? (select all)

1 Self-employment / own business

2 Full-time employee

3 Part-time employee

es_act_o F7) What is the main sector of $(hh_memberX)’s target employer (including family and/or self)? [Record open-ended response].

es_act F7.a) What is the main economic activity that $(hh_memberX)’s target employer (including family and/or self) would undertake?


es_func_o F8) What is the main type of work that $(hh_memberX) is pursuing in his/her search (name of occupation)? [Record open-ended response].

es_func_f F8.a) What is the main type of work that $(hh_memberX) is pursuing in his/her search (name of occupation)?



[First ask the respondent for an open-ended answer then select and verify the ISCO-08 code with him/her].


es_func F8.b) Please select an occupation sub-category.

Education and Training

ed_student G1) Has $(hh_memberX) been a student or apprentice in regular education during the last four weeks?

1 Yes, has been a student or an apprentice

2 Is in regular education but on holiday

0 No, has not been a student or apprentice

ed_highest G2) What is the highest level of education completed by $(hh_memberX)? (select one)

1 Did not finish primary school → ed_year

2 (Primary) Elementary education (classes I-V) → ed_year

3 Lower secondary education (classes VI-IX) → ed_year

4 Upper secondary – general (gymnasium)

5 Upper secondary – vocational (2-3 years)

6 Post secondary - vocational (high vocational school 4-5 years or post-secondary vocational education 1-2 years)

7 Tertiary / university

8 Post graduate / master

9 Doctorate

es_field_o G3) What is the primary field of $(hh_memberX) highest level of education completed: $(ed_highest)? [Record open-ended response].

ed_field G3.a) What is the primary field of $(hh_memberX)’s highest level of education completed: $(ed_highest)? [Verify selection with respondent].

1 General programs



2 Teacher training and education science

3 Humanities, languages and arts

4 Social sciences

5 Business

6 Law

7 Life science (including biology and environmental

science)

8 Physical science (including physics, chemistry,

and earth science)

9 Mathematics and statistics

10 Computer science

11 Computer use

12 Engineering

13 Manufacturing and construction

14 Agriculture and veterinary

15 Health and welfare

16 Crafts and trades

17 Administrative and clerical

18 Services

19 Unknown

ed_field_c G4) Is $(hh_memberX)’s current or most recent job closely related to his/her primary field of study?

1 Yes → ed_field_y

0 No

2 He/she has never worked → ed_year

ed_field_w G4.a) Why is $(hh_memberX) not working in his/her field of study?

1 There are no jobs available in my field of study



2 Other (specify)

ed_field_p G4.b) Has $(hh_memberX)’s ever done any work that is closely related to his/her field of study?

1 Yes

0 No → ed_year

ed_field_y G4.c) How many total years has $(hh_memberX) worked in job(s) related to his/her field of study?

>1 → ed_year

ed_field_m G4.d) How many total months has $(hh_memberX) worked in job(s) related to his/her field of study? [If less than one month, enter 1.]

ed_year G5) How many years ago did $(hh_memberX) finish the highest level of education?

Self-Declared Labor Status

sdls H1) During the last week in which of the following categories would $(hh_memberX) classify him/herself? [Please read out all options to respondent before selecting one.]

1 Employed, including self-employment, unpaid work for a

family business or farm, or an apprenticeship or paid traineeship, etc.

2 Unemployed

3 Pupil, student, further training, unpaid work

experience

4 In retirement or early retirement

5 Permanently disabled

6 Fulfilling domestic tasks

7 Other (specify)

responder H2) Who answered the questions for $(hh_memberX)?



1 Person him/herself

2 Other household member

Thank you. This concludes the survey. In the next week, someone may contact you by phone or in person to verify some of the information you have given me. The purpose is only to ensure that I am asking questions and recording your answers correctly. It is therefore for quality control purposes only and will not be used for the research study.

household_image With permission, please take a photo of the façade of the building.

household_notes Please enter any additional important notes for this household.

End

149


NACE REV 2 CODES A Agriculture, forestry and fishing B Mining and quarrying C Manufacturing D Electricity, gas, steam and air conditioning supply E Water supply; sewerage, waste management and remediation activities F Construction G Wholesale and retail trade; repair of motor vehicles and motorcycles H Transportation and storage I Accommodation and food service activities J Information and communication K Financial and insurance activities L Real estate activities M Professional, scientific and technical activities N Administrative and support service activities O Public administration and defence; compulsory social security P Education Q Human health and social work activities R Arts, entertainment and recreation S Other service activities T Activities of households as employers; undifferentiated goods- and services-producing

activities of households for own use

U Activities of extraterritorial organisations and bodies ISCO-08 CODES

Managers 11 Chief Executives, Senior Officials and Legislators 12 Administrative and Commercial Managers 13 Production and Specialized Services Managers 14 Hospitality, Retail and Other Services Managers

Professionals 21 Science and Engineering Professionals 22 Health Professionals 23 Teaching Professionals 24 Business and Administration Professionals 25 Information and Communications Technology Professionals 26 Legal, Social and Cultural Professionals

Technicians and Associate Professionals 31 Science and Engineering Associate Professionals 32 Health Associate Professionals 33 Business and Administration Associate Professionals 34 Legal, Social, Cultural and Related Associate Professionals 35 Information and Communications Technicians

Clerical Support Workers 41 General and Keyboard Clerks 42 Customer Services Clerks 43 Numerical and Material Recording Clerks 44 Other Clerical Support Workers

Services and Sales Workers 51 Personal Services Workers 52 Sales Workers

150


53 Personal Care Workers 54 Protective Services Workers

Skilled Agricultural, Forestry and Fishery Workers 61 Market-oriented Skilled Agricultural Workers 62 Market-oriented Skilled Forestry, Fishery and Hunting Workers 63 Subsistence Farmers, Fishers, Hunters and Gatherers

Craft and Related Trades Workers 71 Building and Related Trades Workers (excluding Electricians) 72 Metal, Machinery and Related Trades Workers 73 Handicraft and Printing Workers 74 Electrical and Electronic Trades Workers 75 Food Processing, Woodworking, Garment and Other Craft and Related Trades Workers

Plant and Machine Operators and Assemblers 81 Stationary Plant and Machine Operators 82 Assemblers 83 Drivers and Mobile Plant Operators

Elementary Occupations 91 Cleaners and Helpers 92 Agricultural, Forestry and Fishery Labourers 93 Labourers in Mining, Construction, Manufacturing and Transport 94 Food Preparation Assistants 95 Street and Related Sales and Services Workers 96 Refuse Workers and Other Elementary Workers

Armed Forces Occupations 01 Commissioned Armed Forces Officers 02 Non-commissioned Armed Forces Officers 03 Armed Forces Occupations, Other Ranks Other (specify)

151


Extended Interview

EXTENDED INTERVIEW Consent (did not complete household roster [consentscript_nr]): My name is [NAME] and I am working with IDRA, a data collection and research firm in Kosovo. We are gathering information on the workforce status in your community in order to improve projects in this and other communities. Our study is funded by the Millennium Challenge Corporation (MCC), a U.S. agency that provides assistance to other countries’ development projects. This research is being carried out by IDRA in collaboration with Social Impact, a management consulting firm based in the Washington D.C. area. Your household was randomly selected to participate in this study, and you were randomly chosen from a list of eligible household members to participate in an extended interview. If you agree to participate, I will interview you about your access to resources to start a business, interest and involvement in entrepreneurship, experience with workplace discrimination, and beliefs on gender norms. I will also work with you to fill in a retrospective diary of your activities over the past 24 hours using a paper questionnaire. This interview is expected to take 60 minutes. Any information you provide that can identify you or other members of your household will be kept strictly confidential by the parties conducting this study, including MCC employees, employees of the survey firm, and the researchers, to the maximum extent permitted by the laws of the United States and the laws of Kosovo. The information collected will be used for statistical purposes only and will not be used for determining any sort of benefits or punish you for anything so please answer honestly. Your participation is voluntary and you may choose not to answer any or all questions for any reason. In other words, you have the alternative not to participate and there will be no consequences for nonparticipation. To compensate you for your time, you will be provided with a gift for completing the survey. More broadly, members of your community and country may also benefit from this study by helping MCC understand how to best improve labor force outcomes in Kosovo. You may contact Liresa Shala, the administrator of the IDRA branch in Pristina, at [email protected] UUUUUUUU52T52T52T52T52T52T52T52T, or Richard Columbia, Co-Chair of the Institutional Review Board at Social Impact at [email protected] 52T52T52T52T52T52T52T52T if you have any questions, concerns or complaints about the study or your rights as a participant. If you have any questions, please feel free to ask me at any time. Abridged consent (completed household roster [consentscript_r]): Among your eligible household members, you were randomly selected to participate in an extended interview. If you agree to participate, I will interview you about your access to resources to start a business, interest and involvement in entrepreneurship, experience with workplace discrimination, and beliefs on gender norms. I will also work with you to fill in a retrospective diary of your activities over the past 24 hours using a paper questionnaire. This extended interview is expected to take 60 minutes. Any information you provide that can identify you or other members of your household will be kept strictly confidential by the parties conducting this study, including MCC employees, employees of the survey firm, and the researchers, to the maximum extent permitted by the laws of the United States and the laws of Kosovo. The information collected will be used for statistical purposes only and will not be used for determining any sort of benefits or punish you for anything so please answer honestly. Your participation is voluntary and you may choose not to answer any or all questions for any reason. In other words, you have the alternative not to participate and there will be no consequences for nonparticipation. To compensate you for your time, you will be provided with a gift for completing the survey. More broadly, members of your community and country may also benefit from this study by helping MCC understand how to best improve labor force outcomes in Kosovo.

mailto:[email protected]


152


You may contact Liresa Shala, the administrator of the IDRA branch in Pristina, at [email protected] 52T52T52T52T52T52T52T52T, or Richard Columbia, Co-Chair of the Institutional Review Board at Social Impact at [email protected] 52T52T52T52T52T52T52T52T if you have any questions, concerns or complaints about the study or your rights as a participant. If you have any questions, please feel free to ask me at any time.


Basic Information

f_date Please record today’s date (DDMMYYYY):

f_region Please record the region where the survey takes place:

{REGION LIST}

f_munic Please record the municipality where the survey takes place:

{filter:f_regon MUNICIPALITY LIST}

f_sett Please record the settlement where the survey takes place:

{filter:f_munic SETTLEMENT LIST}

f_ea Please record the enumeration area where the survey takes place:

{filter:f_sett EA LIST}

f_hhid Please enter the sampled household’s unique identification number (Household ID):

f_structure_id Please record the structure ID where the survey takes place:

f_hh_num Please record the Household number where the survey takes place: Please confirm that the Household ID you selected is correct. If not, go back and select the correct one (you might need to correct the structure ID, settlement, municipality and/or region if the correct Household ID was not showing as an option in the Household ID field).

f_rid Please enter the respondent’s unique household identification number (Respondent ID):

f_geocode Please record the geocoordinates for this household.

f_roster Did this respondent also complete the household roster?

1 Yes → consentscript_r

0 No → consentscript_nr



153


f_consent [Read relevant consent script] Does respondent consent to participate in the survey?

1 Yes → f_name

0 No

f_consent_n Please specify reason for non-consent (if provided): (select all)

1 I am unable to spare the time → end

2 I do not want to spare the time → end

3 I am not interested in the topic of this survey → end

4 I am concerned about privacy → end

5 I am concerned about safety → end

6 Other (specify) → end

f_name Enter respondent’s first name:

f_sex What is ${f_name}’s sex? (select one)

1 Male

2 Female

f_marital What is your legal marital status? (select one)

1 Single

2 Married or factual relationship

3 Widowed

4 Divorced or legally separated

5 Other (specify)

energytype What kind of energy do you use to heat your home? (select one)

1 Electricity

2 Wood

3 Coal

4 Mazut

5 Natural gas

pc_land_hm How many Aris of agricultural land does your household currently own?

154


Household Decision Making I am now going to ask you a series of questions about who in your household makes spending decisions for 11 different items/expenses. The purpose of this module is to understand how household decision-making relates to labor force outcomes. Remember, your responses will be kept confidential so please answer honestly.

Which member of the family ultimately decides how much to spend on $(ITEM)? (select one) 1 Self 2 Spouse/partner 3 Joint self and spouse 4 Joint self and others 5 Joint spouse and others 6 Other HH member(s)

Who has a major say in how much to spend on $(ITEM)? (select all) 1 Self 2 Spouse/partner 3 Other HH member(s)

To what extent do you feel you can influence spending decisions on $(ITEM)? 1 To a great extent 2 Somewhat 3 Very little 4 Not at all

Var Item

food Food

cloth Clothing

dur_s Small consumer durables (carpets, cookware)

dur_l Large consumer durables (fridge, TV, sofa)

educ Education

health Health

rep_min Home improvement/repair – minor

rep_maj Home improvement/repair – major

land Land purchase or sale

house House purchase or sale

energy

Kind of Energy

155


Access to Productive Capital I am now going to ask you a series of questions on access to 15 different types of productive capital. The purpose of this module is to understand how access to productive capital relates to labor force outcomes, including investments in business.

Var Productive Capital

Does anyone in your household currently own $(ITEM)? 1 Yes 0 No

How many of $(ITEM) does your household currently own?

Who would you say owns most of $(ITEM)? 1 Self 2 Spouse/partner 3 Joint self and spouse 4 Joint self and others 5 Joint spouse and others 6 Other HH member(s)

Who would you say can decide whether to sell, give away, mortgage, and/or rent out $(ITEM) most of the time? 1 Self 2 Spouse/partner 3 Joint self and spouse 4 Joint self and others 5 Joint spouse and others 6 Other HH member(s)

pc_land Agricultural land (Aris) Note: 1 hectare=100 Ari

pc_lstock_l Large livestock (oxen, cattle)

pc_lstock_s Small livestock (goats, pigs, sheep)

pc_poultry Chickens, Ducks, Turkeys, Pigeons

pc_fish Fish pond or fishing equipment

pc_farm_nm Farm equipment (non-mechanized)

pc_farm_m Farm equipment (mechanized)

pc_bus_eq Nonfarm business equipment

pc_house Housing structures (residential)

pc_comm_re Non-residential structures (commercial and other)

pc_comp Computer or laptop

pc_dur_l Large consumer durables (fridge, TV, sofa)

pc_land_o Other land not used for agriculture (pieces, residential or commercial)

156


pc_transp_b Means of transportation – non-motorized (bicycle)

pc_transp_m Means of transportation – motorized (motorcycle, car)

Access to Credit I am now going to ask you a series of questions on access to credit and borrowing patterns. The purpose of this module is to better understand credit constraints in Kosovo as well as how access to credit relates to labor force outcomes, including investments in business. I will start by asking you about your borrowing history with different types of lenders over the past 12 months. Note that you should consider yourself a borrower if you were at least 50 percent responsible for the decision to borrow and spend the money, even if you were not the one who actually took out the loan.

Over the past 12 months, what is the total amount (€) that you borrowed from each of the following sources? You should consider yourself a borrower if you were at least 50 percent responsible for the decision to borrow and spend the money, even if you were not the one who took the loan.

If amount borrowed from source is > 0 If (more) credit had been available to you from this source, would you personally have used it? 1 Yes 0 No

YES, would have used

more from source NO, would not have

used more from source

157


Source Lending source

What was the main purpose for which you used the (total amount of money)? 1 Start a new business 2 Support existing business 3 Pay medical expenses or debt 4 Pay other (non-medical) debt 5 Pay education expenses 6 Buy consumable goods 7 Buy durable goods 8 Home construction / improvement

9 Utilities 10 Give to family member 11 Other (specify)

How much of the (total amount of money) was used for (main purpose)?

What was the main purpose for which you used the remaining amount of (total amount of money)? 1 Start a new business 2 Support existing business 3 Pay medical expenses or debt 4 Pay other (non-medical) debt 5 Pay education expenses 6 Buy consumable goods 7 Buy durable goods 8 Home construction / improvement


What is the main purpose for which you would have used additional credit from (source of money)? 1 Start a new business 2 Support existing business 3 Pay medical expenses or debt 4 Pay other (non-medical) debt 5 Pay education expenses 6 Buy consumable goods 7 Buy durable goods 8 Home construction / improvement


Why would you not have borrowed more from (source of money)? 1 Have enough money 2 Don’t qualify/no collateral 3 Cannot pay back 4 Afraid to lose collateral 5 Interest/costs too high 6 Not allowed to borrow/family dispute in

borrowing decision(s) 7 Lender not available/too far 8 Other (specify)

form Bank/financial institution (non-MFI)

mfi MFI (including NGO and group-based lending)

inforl Informal money lender (charges interest)

friend Friends or relatives (don’t charge interest)

If total borrowed from ALL sources = 0: borrowednone Why didn’t you borrow from any of these credit sources?

158


Access to Credit (continued)

credit_de Who in your household is primarily responsible for decisions on borrowing?

1 Self

2 Spouse/partner

3 Self and spouse/partner jointly

4 Self and others jointly

5 Spouse/partner and others jointly

6 Other household member(s)

Economic Independence

ei_wage In the past year, have you personally earned any wage or salary?

1 Yes

0 No → ei_acct

ei_wage_d Over the past year, who made decisions about how the majority of your wage or salary income was spent?

1 Self

2 Spouse/partner

3 Self and spouse/partner jointly

4 Self and others jointly

5 Spouse/partner and others jointly

6 Other household member(s)

ei_acct Do you have a savings account for which you are a legal account holder/signer?

1 Yes

0 No → ent_bus

ei_acct_s Do you have a savings account in your name only?

1 Yes

0 No

Entrepreneurship (Present) This next module is focused on entrepreneurship and will include questions on your involvement in businesses, in the past, present, and future. I will also ask some questions about attitudes toward entrepreneurship in Kosovo.

159


ent_bus_i Have you, alone or with others, already started or are currently trying to start a new business, including any self-employment or selling any goods or services to others? (select all)

1 Yes, I have already started a business (and I am still → ent_sal

involved in it)

2 Yes, I am currently trying to start a new business → ent_plan

0 No, I am not currently involved in my own business → ent_bus

ent_main [If both 1 and 2 are selected] Which do you consider to be your main business?

1 One that I have already started

2 One that I am currently trying to start → ent_plan

ent_sal Has the business paid any salaries, wages, or payments in kind, including your own, for more than three months? “Payment in kind” refers to goods or services provided as payments for work rather than cash.

1 Yes

0 No → entre_ows

entre_wgs In what year did your business first pay salaries, wages, or payments in kind, including your own? (YYYY)

entre_ows How many people, including yourself, own this business?

entre_emp Not counting the owners, how many people are currently working for this business?

ent_reg Has the business been formally registered with the Kosovo Business Registration Agency (KBRA)?

1 Yes

0 No → ent_reg_r

entre_emp_l Of the $(entre_emp) person(s) working for this business, for how many do you pay pension contribution?

→ ent_choice

ent_reg_r Why have you not formally registered your business with KBRA?

160


1 Don’t know how (or where) to do so → ent_choice

2 Have not had the time → ent_choice

3 Don’t see benefit in doing so → ent_choice

4 Cannot afford to do so → ent_choice

5 Other (specify) → ent_choice

ent_plan Over the past 12 months, have you done anything to help start this new business, such as looking for equipment or location, organizing a start-up team, working on a business plan, beginning to save money, or any other activity that would help launch a business?

1 Yes

0 No

ent_choice Why are you involved in this business? (select one)

1 Because I want to take advantage of business

opportunity

2 I would prefer to do something else, but there are no

other opportunities available to me

3 Combination of both 1 and 2

4 Other (specify)

ent_unf Will all, some, or none of your customers or potential customers consider the business product(s) or service(s) new and unfamiliar?

1 All

2 Some

3 None will consider this new and unfamiliar

ent_act What is the main economic activity that this business is undertaking or will undertake?


ent_new Right now, are there many, few or no other businesses offering the same/similar product(s) or service(s) to your customers/potential customers?

1 Many business competitors

2 Few business competitors

3 No business competitors

161


ent_own Will/do you personally own all, part or none of this business?

1 All → ent_sup

2 Part

3 None → ent_sup

ent_own_p Approximately what percentage of this business do you own?

ent_sup What was the main source of start-up capital for this business? [Read out options before selecting]

1 Own resources / savings

2 Formal loan

3 Informal loan

4 Remittances

5 Other (specify)

ent_ment Various people may give you advice on your business. Have you received advice from any of the following? (select all) [Read out options and allow respondent to say yes or no for each option]

1 Spouse or partner

2 Parents

3 Other family or relatives

4 Current work colleagues

5 Current boss

6 Somebody who is starting or has started a business

7 Somebody with much business experience

8 Researcher or inventor

9 Possible investor

10 Bank

11 Lawyer

12 Accountant

13 Public advising service for businesses

162


14 Another firm

15 A customer

16 NGO or non-profit organization

17 Other (specify)

ent_grow Have you been able to grow your business as much as you want to?

1 Yes → ent_inc

0 No

ent_const What is the main obstacle in growing your business?

ent_inc In the past 12 months, what proportion (%) of your personal income came from this business?

Entrepreneurship (Future and Past)

ent_bus Are you, alone or with others, expecting to start a new business, including any self-employment, within the next 3 years?

1 Yes

0 No

ent_close Have you, in the past 12 months, sold, shut down, discontinued or quit a business you owned and managed, any form of self-employment, or selling goods or services to anyone?

1 Yes

0 No → ent_opp

ent_stat Did the business continue its business activities after you quit?

1 Yes

0 No

ent_opp In the next 6 months, will there be good opportunities for starting a business in the area where you live?

1 Yes

0 No

163


ent_skill Do you have the knowledge, skill and experience required to start a new business?

1 Yes

0 No

ent_des In Kosovo, do most people consider starting a new business a desirable career choice?

1 Yes → ent_stat

0 No

ent_des_w Why is starting a new business not considered a desirable career choice in Kosovo?

ent_resp In Kosovo, do people who are successful at starting a new business have a higher level of status and respect?

1 Yes

0 No

Income This next module is focused on income, remittances, and working abroad. Remember that all of your responses are strictly confidential, so please answer honestly. This data will only be used for statistical purposes and will not be used for determining any benefits or other social scheme .

income_hh What is the total ANNUAL income in Euros (€) of all the members of your household (including remittances), including your income, as one combined figure?

income_p How much of your total ANNUAL household income (€) reported just now do you personally contribute?

Remittances and Working Abroad

rem_port What proportion (%) of your total household income consists of foreign remittances received from outside of Kosovo?

rem_tot Over the past 12 months, what is the total amount of foreign remittances that you have received (€)?

0 → rem_abr

rem_purp What is the main purpose for which you used the €$(rem_tot)? (select one)

1 Start a new business

164


2 Support existing business

3 Pay medical expenses or medical debt

4 Pay other (non-medical) debt

5 Pay education expenses

6 Buy consumable goods

7 Buy durable goods

8 Home construction / improvement

9 Utilities

10 Give to family member

11 Other (specify)

rem1_tot How much of the €$(rem_total) you received was used for the main purpose you just specified (€)?

rem2_purp What is the main purpose for which you used the remaining amount of $(rem_tot)-$(rem1_tot)? (select one)

1 Start a new business

2 Support existing business

3 Pay medical expenses or debt

4 Pay other (non-medical) debt

5 Pay education expenses

6 Buy consumable goods

7 Buy durable goods

8 Home construction / improvement

9 Utilities

10 Give to family member

11 Other (specify)

rem_abr In the past 12 months, have you worked outside of Kosovo?

1 Yes

0 No → rem_abr_f

165


rem_count In which countries have you worked in the past 12 months? (select all)

1 Albania

2 Austria

3 Belgium

4 Denmark

5 France

6 Germany

7 Greece

8 Italy

9 Netherlands

10 Norway

11 Slovenia

12 Sweden

13 Switzerland

14 United Kingdom

15 United States

16 Other (specify)

rem_countX In the past 12 months, what is the total number of months that you worked in $(rem_countX)?

rem_abr_f In the next 12 months, do you plan to work outside of Kosovo?

1 Yes

0 No

rem_count_f In which countries do you plan to work in the next 12 months? (select all)

1 Albania

2 Austria

3 Belgium

4 Denmark

166


5 France

6 Germany

7 Greece

8 Italy

9 Netherlands

10 Norway

11 Slovenia

12 Sweden

13 Switzerland

14 United Kingdom

15 United States

11 Other (specify)

Workplace Discrimination This next module is focused on workplace discrimination and gender norms and attitudes in Kosovo. Your responses are confidential so please answer honestly. As with all modules in this survey, you do not have to respond to any question for any reason, including if you are uncomfortable providing a truthful answer.

f_emp Have you ever been in regular employment before (job as employee or unpaid family member)?

1 Yes

0 No → f_care

f_discr In your past or present employment, do you feel you are/were treated equally within the workplace by your employer(s) when compared with members of the opposite sex that you work with? This includes both negative and positive treatment.

1 Yes → f_care

0 No

2 N/A → f_care

Was this unequal treatment positive or negative?

f_discr_pn

1 Positive

2 Negative

f_discr_h In what ways were you not treated equally?

167


f_adv Do you feel this unequal treatment has prevented you from advancing professionally?

1 Yes

0 No

f_care In order to care for a child or family member, have you ever: (select all) [Read out options and allow respondent to say yes or no for each option]

1 Reduced work hours

2 Taken a significant amount of time off

3 Quit job

4 Turned down a promotion → f_equal

5 None of the above → f_equal

f_hurt Do you feel that the time away has hurt your career?

1 To a great extent

2 Somewhat

3 Very little

4 Not at all

f_ccare Has access to child care (or lack thereof) influenced your career decisions?

1 Yes

0 No → f_transp

2 Not Applicable → f_transp

f_ccare_y In what ways has child care access influenced your career decisions?

f_transp Has lack of access to safe, reliable, / and/or affordable transport influenced your career decisions?

1 Yes

0 No → f_equal

168


2 Not Applicable → f_equal

f_transp_y In what ways has lack of access to safe, reliable, and/or affordable transport influenced your career decisions?

f_equal Do you believe that men and women in Kosovo are generally treated equally in the workplace?

1 Yes → f_focus

0 No

f_equal_h In what ways are they not treated equally?

f_focus Of the people you know who are around your age, who is more focused on their career?

1 Women more than men

2 Men more than women

3 No difference

f_discr_f Has concern about discrimination influenced your career decisions?

1 Yes

0 No

f_discr_fh In what ways has concern about discrimination influenced your career decisions?

Gender Norm Attitudes Scale

I will now read a series of statements related to gender norm attitudes. For each statement, please tell me if you agree or disagree with that statement.

Agree Disagree

gnas_1 It is important that sons have more education than daughters

gnas_2 Daughters should be sent to school only if they are not needed

to help at home

gnas_3 The most important reason that sons should be more educated

than daughters is so they can better look after their parents when they are older

gnas_4 If there is a limited amount of money to pay for tutoring, it should

be spent on sons first

gnas_5 A woman should take good care of her own children and not

worry about other people’s affairs

169


gnas_6 Women should leave politics to the men

gnas_7 A woman has to have a husband or sons or some other male

kinsman to protect her

gnas_8 The only thing a woman can really rely on in her old age is her

sons

gnas_9 A good woman never questions her husband’s opinions, even

if she is not sure she agrees with them

gnas_10 When it is a question of children’s health, it is best to do what the

father wants

gnas_11 Daughters should be able to work outside the home after they

have children if they want to

gnas_12 Daughters should have just the same chance to work outside of

homes as sons

gnas_13 Daughters should be told that an important reason not to have

too many children is so they can work outside the home and earn money

gnas_14 I would like my daughter to be able to work outside the home so

she can support herself if necessary

gnas_15 Daughters should have the same inheritance rights as sons

gnas_ext It’s more important for men in the household to be employed than

women

170


TIME USE SURVEY

Recall period: Respondents will be asked to reflect on the most recent complete 24-hour period that ended at 4:00 AM. For example, if it is 8:00 PM on a Thursday at the time of the survey, the respondent will reflect on the 24-hour period beginning 4:00 AM on Wednesday and ending 3:59 AM on the present day (Thursday).

Time segments: The 24-hour period beginning 4:00 AM and ending 3:59 AM is segmented into 24 hours and four 15-minute time blocks per hour (96 segments total).

Primary activity: For each 15-minute segment, record the respondent’s primary activity in his or her own words. If the activity extends beyond 15 minutes, you may use a line with an arrow to indicate the length of the activity.

Secondary activity: When appropriate, a secondary activity may be recorded in the secondary activity column (again, using the respondent’s own words).

Persons present: For each 15-minute segment, record whether the respondent was: C – In the presence of a child household member under the age of 9 H – In the presence of other household member(s) (9 and up) O – In the presence of other persons (non-household members)

Activity codes: At the end of each day, enumerators are responsible for converting the open response for each 15-minute segment into one of the 2008ACL activity codes per the guidelines below.

Category 2008ACL Activity Codes

Personal Care

0.1.1 sleep 0.1.2 sick in bed 0.2.1 eating 0.3.1 washing and dressing 0.3.9 other or unspecified personal care

Employment 1.1.1 working time (including coffee breaks and travel at work) 1.2.1 activities related to employment – lunch break 1.2.2 activities related to employment – job search 1.2.9 activities related to employment – other or unspecified

Studying 2.0.0 unspecified study 2.1.1 school or university – classes and lectures 2.1.2 school or university – homework 2.2.1 free time study

Household and Family Care

3.1.1 food preparation, baking and preserving 3.1.2 dish washing 3.8.1 childcare – physical care and supervision 3.8.2 childcare – teaching the child 3.8.3 childcare – reading, playing and talking with child 3.8.4 childcare – accompanying child 3.8.9 childcare – other or unspecified 3.9.1 help to adult family member – physical care of dependent adult hh member 3.9.2 help to adult family member – other help of dependent adult hh member 3.9.9 help to adult family member – help to non-dependent adult hh member 3.2.1 household upkeep - cleaning dwelling 3.2.2 household upkeep - cleaning garden

171


3.2.3 household upkeep - heating and water 3.2.4 household upkeep - arranging household goods and materials 3.2.9 household upkeep – other or unspecified 3.3.1 making and care for textiles - laundry 3.3.2 making and care for textiles - ironing 3.3.3 making and care for textiles – handicraft and producing textiles 3.3.9 making and care for textiles – other or unspecified 3.4.1 gardening and pet care - gardening 3.4.2 gardening and pet care – tending domestic animals 3.4.3 gardening and pet care – caring for pets 3.4.4 gardening and pet care – walking the dog 3.4.9 gardening and pet care – other or unspecified 3.5.1 construction and repairs – house construction and renovation 3.5.2 construction and repairs – repairs to dwelling 3.5.3 construction and repairs – making, repairing and maintaining equipment 3.5.4 construction and repairs – vehicle maintenance 3.5.9 construction and repairs – other or unspecified 3.6.1 shopping and services – shopping 3.6.2 shopping and services – commercial and administrative services 3.6.3 shopping and services – personal services 3.6.9 shopping and services – other or unspecified 3.7.1 household management 3.0.0 Unspecified household and family care

Voluntary work and meetings

4.1.1 Organizational work (work for or through an organization) 4.2.1 Informal help to other households – construction and repairs as help 4.2.2 Informal help to other households – help in employment and farming 4.2.3 Informal help to other households – care of own children living in another hh 4.2.4 Informal help to other households – other childcare as help to another hh 4.2.5 Informal help to other households – help to an adult of another hh 4.2.9 Informal help to other households – other or unspecified 4.3.1 participatory activities – meetings 4.3.2 participatory activities – religious activities 4.3.9 participatory activities – other or unspecified

Social life and entertainment

5.1.1 social life – socializing with family 5.1.2 social life – visiting and receiving visitors 5.1.3 social life – celebrations 5.1.4 social life – telephone conversation 5.1.9 social life – other or unspecified 5.2.1 entertainment and culture – cinema 5.2.2 entertainment and culture – theater and concerts 5.2.3 entertainment and culture – art exhibitions and museums 5.2.4 entertainment and culture – library 5.2.5 entertainment and culture – sports events 5.2.9 entertainment and culture – other or unspecified 5.3.1 resting – time out

172


Sports and outdoor activities

6.1.1 physical exercise – walking and hiking 6.1.2 physical exercise – jogging and running 6.1.3 physical exercise – cycling, skiing and skating 6.1.4 physical exercise – ball games 6.1.5 physical exercise – gymnastics and fitness 6.1.6 physical exercise – water sports 6.1.9 physical exercise – other or unspecified 6.2.1 productive exercise (hunting, fishing, picking berries, mushrooms, herbs) 6.3.1 sports related activities

Hobbies and computing

7.1.1 arts and hobbies – arts (visual, performing, literary) 7.1.2 arts and hobbies – collecting 7.1.3 arts and hobbies – correspondence 7.1.9 arts and hobbies – other or unspecified 7.2.1 computing – programming 7.2.2 computing – information by computing 7.2.3 computing – communication by computing 7.2.9 computing – other or unspecified 7.3.1 games – solo games and playing, gambling 7.3.2 games – parlor games and play 7.3.3 games – computer games 7.3.9 games – other or unspecified

Mass media 8.1.1 reading – periodicals 8.1.2 reading – books 8.1.9 reading – other or unspecified 8.2.1 watching tv, video or DVD 8.3.1 listening to radio or recordings

Travel 9.1.0 travel –to/from work 9.2.0 travel – related to study 9.3.6 travel – related to shopping and services 9.3.8 travel – related to childcare 9.3.9 travel – related to other household care 9.4.0 travel – related to voluntary work and meetings 9.5.0 travel – related to social life 9.6.0 travel – related to other leisure 9.8.0 travel – related to changing locality 9.0.0 travel – other or unspecified

Other 9.9.8 auxiliary codes – unspecified leisure time 9.9.9 auxiliary codes – other


Household ID:________________ Respondent ID:________________ Persons Present Primary Activity Code Secondary Activity Code C H O N/A

4:0

0 A

M 4:00 - 4:14

4:15 - 4:29

4:30 - 4:44

4:45 - 4:59

5:0

0 A

M 5:00 - 5:14

5:15 - 5:29

5:30 - 5:44

5:45 -5:59

6:0

0 A

M 6:00 - 6:14

6:15 - 6:29

6:30 - 6:44

6:45 - 6:59

7:0

0 A

M 7:00 - 7:14

7:15 - 7:29

7:30 - 7:44

7:45 - 7:59

8:0

0 A

M 8:00 - 8:14

8:15 - 8:29

8:30 - 8:44

8:45 - 8:59

9:0

0 A

M 9:00 - 9:14

9:15 - 9:29

9:30 - 9:44

9:45 - 9:59

10:0

0A

M 10:00 - 10:14

10:15 - 10:29

10:30 - 10:44

10:45 - 10:59

11:0

0 A

M 11:00 - 11:14

11:15 - 11:29

11:30 - 11:44

11:45 - 11:59

12:0

0 P

M 12:00 - 12:14

12:15 - 12:29

12:30 - 12:44

12:45 - 12:59

1:0

0 P

M 1:00 - 1:14

1:15 - 1:29

1:30 - 1:44

1:45 - 1:59

2:0

0

PM

2:00 - 2:14

2:15 - 2:29


2:30 - 2:44

2:45 - 2:59

3:0

0 P

M 3:00 - 3:14

3:15 - 3:29

3:30 - 3:44

3:45 - 3:59

Persons Present

Primary Activity Code Secondary Activity Code C H O N/A

4:0

0 P

M 4:00 - 4:14

4:15 - 4:29

4:30 - 4:44

4:45 - 4:59

5:0

0 P

M 5:00 - 5:14

5:15 - 5:29

5:30 - 5:44

5:45 -5:59

6:0

0 P

M 6:00 - 6:14

6:15 - 6:29

6:30 - 6:44

6:45 - 6:59

7:0

0 P

M 7:00 - 7:14

7:15 - 7:29

7:30 - 7:44

7:45 - 7:59

8:0

0 P

M 8:00 - 8:14

8:15 - 8:29

8:30 - 8:44

8:45 - 8:59

9:0

0 P

M 9:00 - 9:14

9:15 - 9:29

9:30 - 9:44

9:45 - 9:59

10:0

0 P

M 10:00 - 10:14

10:15 - 10:29

10:30 - 10:44

10:45 - 10:59

11:0

0 P

M 11:00 - 11:14

11:15 - 11:29

11:30 - 11:44

11:45 - 11:59

12:0

0 A

M 12:00 - 12:14

12:15 - 12:29

12:30 - 12:44

12:45 - 12:59


1:0

0 A

M 1:00 - 1:14

1:15 - 1:29

1:30 - 1:44

1:45 - 1:59

2:0

0A

M 2:00 - 2:14

2:15 - 2:29

2:30 - 2:44

2:45 - 2:59

3:0

0 A

M 3:00 - 3:14

3:15 - 3:29

3:30 - 3:44

3:45 - 3:59

Time Use (continued)

Please estimate how many hours that you spend on each of the following activities during a typical weekday (total must equal 24):

tu_pc Personal care (sleeping, eating, etc.)

tu_emp Employment (including own business)

tu_study Studying

tu_cc Child care

tu_ad Help to adult family members

tu_hh Other household care (food mgmt, cleaning, etc.)

tu_vol Voluntary work and meetings

tu_soc Social life and entertainment

tu_sport Sports and outdoor activities

tu_hob Hobbies and computing

tu_mm Mass media

tu_travwk Travel to/from work or for work-related activities

tu_travoth Travel for non-work purposes

tu_oth Other

f_phone Please record respondent’s phone number:

others_p Were there other persons present during this interview?


1 Yes

0 No → end

others_d Please record who was present during this interview and at what points during the interview they were listening:

tusimage Please take a photo of the front side of the completed paper time use diary.

tusimage2 Please take a photo of the back side of the completed paper time use diary.

Thank you. This concludes the survey. In the next week, someone may contact you by phone or in person to verify some of the information you have given me. The purpose is only to ensure that I am asking questions and recording your answers correctly. It is therefore for quality control purposes only and will not be used for the research study.


Annex 2: Weighting Plan

To make sure that the findings from sample data was nationally representative, household and person (or individual) level weights were constructed for each household and its respective household members, which included all members above the age of 15. The construction of household and person weights was based on the sampling strategy, which used a two-stage stratified cluster sampling model. The sampling strategy followed a probability proportional to size approach to first select clusters (or EAs) from within each stratum (or region), and then randomly select 10 households from each sampled EA in the second stage. Given this sampling strategy, the household weight was calculated as the product of the reciprocals of the sampling fractions employed in each stage of selection of the number of sample households. In other words, weight was calculated as the reciprocal of the product of probability of selection at every stage. The method used to achieve this was as follows: First, a sampling fraction, 𝑆𝐸𝑅 , was created for the total number of urban/rural households in the sampled

EAs, 𝑆𝐸𝑅 , as share of the total number of urban/rural households in that region, 𝐻𝑅:

𝑆𝐸𝑅 =𝐻𝐸𝑆

𝐻𝑅

Note: since EAs could be identified as urban or rural, the respective share of each urban/rural EAs would correspond with the total number of urban/rural households in a particular region. Next, per the study’s research design, a sampling fraction, SA, was created in order to calculate the share of the ten sampled households for each sampled EA among the total number of households in that EA, 𝐻𝐸𝑆, as follows:

𝑆𝐴 =10

𝐻𝐸𝑆

Given these respective shares, the household weight, 𝐻𝑊, was calculated as:

𝐻𝑊 =1

10𝐻𝐸𝑆

𝑋𝐻𝐸𝑆

𝐻𝑅

=𝐻𝑅𝑋𝐻𝐸𝑆

𝐻𝐸𝑆𝑋10

Person weight, 𝑃𝑊, was calculated by adjusting the household weight, 𝐻𝑊, by the number of household

members (above the age of 15), 𝐻𝑁, in the respective household, as follows:

𝑃𝑊 = 𝐻𝑊 𝑋 𝐻𝑁

In addition, person weights were adjusted for extended interview non-response among males (which caused our sample to have a higher proportion of females compared to males). To achieve equal weights for any given male or female extended interview, 𝑃𝑊𝐺 , the respective weight of the male or female respondent was adjusted based on the response rate such that 𝑃𝑅 was the realized proportion and 𝑃𝐸 was the expected proportion of males or females, respectively:

𝑃𝑊𝐺 = 𝑃𝑊 𝑋 𝑃𝑅

𝑃𝐸


Annex 3: Regression Analysis

75T75T75T75T75T75T75T75T3.1 Determinants of Aggregate, Youth and Female Employmen 75T75T75T75T75T75T75T75T75T75T75T75T75T75T75T75Tt

(1) (2) (3) (4) (5) (6)

Independent Variables Employ

ment Employ

ment

Youth Employ

ment

Youth Employ

ment

Female Employ

ment

Female Employ

ment

Male 4.43** 4.41** 3.76** 4.32** 15-24 (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

25-34 2.17** 1.34 2.00** 1.74**

35-44 3.05** 2.04** 4.02** 3.17**

45-54 3.02** 1.95** 4.33** 4.74**

55-64 1.91** 1.51* 3.03** 3.31**

65-74 0.42** 0.22** 1.10 1.51

Single (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

Married or factual relationship 1.29** 1.14 1.19** 1.12 0.72** 0.81

Widowed 1.06 1.16 0.57** 0.57

Divorced or legally separated 1.84 0.86 4.43** 2.80 1.07 0.66

No primary education (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

Primary education 1.81** 1.82* 1.84 2.37 1.56** 1.47

Lower secondary education 1.91** 1.66 2.24 2.97* 1.63** 1.76*

Upper secondary - general 3.18** 1.84* 4.67** 6.38** 2.44** 2.67**

Upper secondary - vocational 3.91** 2.18** 5.23** 5.96** 2.88** 2.42**

Post secondary - vocational 2.75** 2.00* 1.48 1.04 3.70** 2.25

Tertiary 7.02** 5.60** 8.92** 11.04** 8.74** 8.49**

Post graduate 12.87** 6.46** 9.83** 25.06** 22.09** 35.15**

Doctorate 14.67** 0.26 28.62** 36.93*

Rural 1.14 1.16 1.18 1.22 1.07 1.00

Summer 0.85** 0.95 0.75** 0.75** 0.85 0.95

Pristina (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

Ferizaj 1.02 1.15 1.06 0.96 0.77** 1.11

Gjakova 2.35** 2.52** 2.13** 1.37 2.72** 2.95**

Gjilan 1.49** 2.05** 1.55** 1.45 1.50** 2.53**

Mitrovica 1.45** 1.84** 1.26** 1.01 2.05** 2.29**

Peja 1.23** 1.89** 1.18** 0.99 1.27** 1.58*

Prizren 1.62** 1.81** 1.70** 1.62** 1.51** 2.64**

Minors (below 15 years) 1.02 1.03 1.05* 1.04 1.01 1.04

Income (Bottom 40 percent) 0.58** 0.81* 0.91

Remittance Receiving household 0.82* 1.01 1.29*

Plan to work outside of Kosovo 1.07 Male Gender Norms Index 0.99*

Female Gender Norms Index 1.01**

Constant 0.04** 0.08** 0.03** 0.03** 0.05** 0.04**

Observations 31,182 6,533 8,096 4,536 15,755 4,282

** p<0.01, * p<0.05


75T75T75T75T75T75T75T75T3.2 Determinants of Aggregate, Youth and Female Activity/Labor Force Participatio 75T75T75T75T75T75T75T75T75T75T75T75T75T75T75T75Tn

(1) (2) (3) (4) (5) (6)

Independent Variables Activity Activity Youth

Activity Youth

Activity Female Activity

Female Activity

Male 4.64** 5.02** 2.99** 3.32**

15-24 (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

25-34 2.46** 1.14

1.96** 2.29**

35-44 3.10** 1.70**

3.38** 3.68**

45-54 2.57** 1.47

3.14** 4.85**

55-64 1.42** 0.97

2.01** 3.01**

65-74 0.26** 0.11**

0.74 1.47

Single (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

Married or factual relationship 1.01 0.80 0.89* 0.84 0.55** 0.46**

Widowed 0.87 0.89

0.47** 0.36**

Divorced or legally separated 3.11* 0.66 17.99* 15.34 1.75* 0.31

No primary education (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

Primary education 1.86** 1.97* 1.79 3.85* 1.51** 1.57

Lower secondary education 1.89** 1.77* 2.43** 4.41** 1.59** 1.82**

Upper secondary - general 3.24** 1.99* 5.38** 12.31** 2.49** 3.06**

Upper secondary - vocational 4.71** 2.65** 7.82** 15.19** 3.73** 3.81**

Post secondary - vocational 4.77** 3.41** 3.63** 4.71 5.26** 4.03**

Tertiary 11.25** 9.33** 17.66** 42.62** 15.23** 21.68**

Post graduate 33.20** 16.76** 68.62** 123.00** 40.17** 74.62**

Doctorate 23.53** 3.32

27.01** 19.65*

rural 1.07 1.08 1.07 0.96 1.01 0.92

summer 0.83** 0.89 0.73** 0.69** 0.83* 0.90

Pristina (omitted) (omitted) (omitted) (omitted) (omitted) (omitted)

Ferizaj 0.77** 0.99 0.81** 0.79 0.69** 0.91

Gjakova 1.80** 2.17** 1.44** 0.95 2.07** 1.96**

Gjilan 1.32** 1.76** 1.14** 0.97 1.44** 2.12**

Mitrovica 1.20** 1.65** 1.01 0.75 1.64** 1.68**

Peja 0.98 1.76** 0.84** 0.76 1.02 1.12

Prizren 1.39** 1.70** 1.43** 1.42* 1.31** 1.93**

Minors (below 15 years) 1.02 1.04 1.04 1.01 0.99 1.03

Income (Bottom 40 percent)

0.69**

1.13

1.05

Remittance Receiving household

0.81*

1.19

1.28*

Plan to work outside of Kosovo

1.78**

Male Gender Norms Index

0.99

Female Gender Norms Index

1.01**

Constant 0.08** 0.18** 0.08** 0.05** 0.13** 0.08**

Observations 30,921 6,472 8,021 4,486 15,627 4,235

** p<0.01, * p<0.05


Annex 4: Disaggregated Indicators

Employment status

75T75T75T75T75T75T75T75TOccupation by Age

Occupation 15-24 (%)

25-34 (%)

35-44 (%)

45-54 (%)

55-64 (%)

65-74 (%)

Total (%)

Managers 2.0 6.2 6.0 7.1 5.8 2.6 5.5

Professionals 5.9 14.3 10.2 11.6 16.4 4.7 11.5

Technicians and Associate Professionals 1.2 1.6 1.1 1.9 2.3 0.0 1.6

Clerical Support Workers 3.5 4.5 3.4 4.6 4.4 0.0 4.0

Services and Sales Workers 27.7 20.9 12.6 9.7 9.4 6.5 16.3

Skilled Agricultural, Forestry and Fishery Workers 6.7 3.8 8.8 9.7 12.5 19.7 8.0

Craft and Related Trades Workers 16.9 18.5 17.9 14.4 12.0 5.0 16.1

Plant and Machine Operators and Assemblers 2.8 4.8 5.8 6.5 4.7 2.3 4.9

Elementary Occupations 32.5 23.5 31.5 32.0 31.0 59.3 30.2

Armed Forces Occupations 0.7 2.0 2.8 2.5 1.7 0.0 2.0


75T75T75T75T75T75T75T75TOccupation by Education Level

Occupation

No Primary School

(%)

Primary Education

(%)


(%)

Upper Secondary – General

(%)

Upper Secondary –Vocational

(%)

Post Secondary –Vocational

(%)

Tertiary (%)

Post Graduate

(%)

Doctorate (%)

Total (%)

Managers 0.7 0.9 1.4 5.7 5.0 8.4 13.3 19.3 2.7 5.5

Professionals 0.4 0.4 0.3 4.0 5.8 29.1 52.1 58.8 97.3 11.5

Technicians and Associate Professionals

0.0 0.0 0.1 0.9 1.9 6.9 3.7 2.3 0.0 1.6

Clerical Support Workers

0.0 0.6 0.5 5.6 5.0 6.3 6.8 6.0 0.0 4.0

Services and Sales Workers

7.3 4.1 10.2 23.7 21.4 10.3 11.3 4.7 0.0 16.3

Skilled Agricultural, Forestry and Fishery Workers

17.7 22.1 14.0 6.5 6.0 5.3 1.4 0.0 0.0 8.0

Craft and Related Trades Workers

13.8 10.0 18.2 15.7 20.7 11.8 3.9 3.7 0.0 16.1

Plant and Machine Operators and Assemblers

0.0 2.1 4.3 5.6 7.2 3.2 0.7 1.2 0.0 4.9

Elementary Occupations

60.2 59.7 51.1 29.5 24.5 12.8 4.0 1.1 0.0 30.2

Armed Forces Occupations

0.0 0.0 0.1 2.9 2.4 6.1 2.9 3.0 0.0 2.0

Total 1.0 3.0 26.2 15.7 36.6 2.7 12.2 2.5 0.2 100.0


75T75T75T75T75T75T75T75TType of Employer by Region

Type of Employer Ferizaj

(%) Gjakova

(%) Gjilan

(%) Mitrovica

(%) Peja (%)

Pristina (%)

Prizren (%)

Total (%)

Government, Public Sector or Security Forces 7.0 12.3 10.5 13.0 8.7 30.9 17.7 25.1

State-Owned Enterprise 2.0 2.3 6.7 30.1 17.0 40.9 1.1 6.1

Private Company or Enterprise 12.1 10.3 10.5 8.8 10.2 27.3 21.0 60.5

International organization 30.4 2.1 11.7 10.0 2.9 31.5 11.5 1.0

Ngo, Non-Profit or Humanitarian organization 10.0 10.8 7.8 9.7 16.9 33.6 11.3 0.7

Self-Employed 15.9 6.0 8.6 12.4 6.1 35.3 15.7 1.0

Other Private Individual 9.9 22.1 11.1 5.8 1.1 21.6 28.4 5.7

Other 38.5 0.0 44.4 17.2 0.0 0.0 0.0 0.0

75T75T75T75T75T75T75T75TType of Employment (Job Characteristics) by Region

Type of Employment Ferizaj

(%) Gjakova

(%) Gjilan (%)

Mitrovica (%)

Peja (%)

Pristina (%)

Prizren (%)

Total (%)

Employee 10.3 10.8 10.3 11.0 9.6 28.9 19.1 60.8

Self-Employed With Employees 15.0 15.5 17.5 8.6 12.2 16.3 14.9 4.5

Self-Employed Without Employees 11.2 18.3 8.4 9.3 8.4 19.7 24.9 11.6

Unpaid Family Worker 5.0 32.3 11.8 8.9 13.6 8.6 19.9 23.0


75T75T75T75T75T75T75T75TType of Employment (Contractual/Non-contractual) by Region

Region Non-Contractual Job

(%) Contractual Job

(%)

Ferizaj 38.2 61.8

Gjakova 44.1 55.9

Gjilan 42.6 57.4

Mitrovica 36.4 63.6

Peja 34.5 65.6

Pristina 31.2 68.8

Prizren 59.6 40.4

Total 40.8 59.2

75T75T75T75T75T75T75T75TType of Employer by Education Level

Type of Employer

No Primary School

(%)

Primary Education

(%)


(%)


(%)


(%)

Post Secondary –Vocational

(%)

Tertiary (%)

Post Graduate

(%)

Doctorate (%)

Total (%)

Government, Public Sector or Security Forces

6.0 5.2 7.7 15.8 18.4 59.2 54.2 62.1 57.3 25.1

State-Owned Enterprise 1.1 0.0 2.8 3.5 7.4 13.4 7.3 8.6 13.4 6.1

Private Company or Enterprise

76.4 74.0 76.4 71.1 66.7 22.6 33.3 28.1 29.4 60.5

International organization 0.0 0.0 0.5 1.2 1.0 0.0 1.6 0.7 0.0 1.0

Ngo, Non-Profit or Humanitarian organization

0.0 0.0 0.4 1.1 0.4 0.5 1.2 0.5 0.0 0.7

Self-Employed 1.9 3.3 1.5 1.0 1.1 0.8 0.1 0.0 0.0 1.0

Other Private Individual 14.8 17.5 10.7 6.3 5.0 3.5 2.2 0.1 0.0 5.6

Other 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0


75T75T75T75T75T75T75T75TType of Employment (Job Characteristics) by Education Level

Type of Employment

No Primary School

(%)

Primary Education

(%)


(%)


(%)


(%)

Post Secondary-Vocational

(%)

Tertiary (%)

Post Graduate

(%)

Doctorate (%)

Total (%)

Employee 18.0 16.8 41.9 59.5 68.3 76.3 84.8 92.8 96.9 60.8

Self-Employed with Employees

0.3 1.9 2.6 6.2 5.3 5.3 5.4 2.4 1.5 4.5

Self-Employed Without Employees

14.2 14.7 13.8 12.9 12.4 7.3 4.4 2.9 1.6 11.6

Unpaid Family Worker 67.5 66.6 41.8 21.4 14.0 11.1 5.4 1.9 0.0 23.1

Total Sample 3.3 6.3 36.6 15.2 27.4 2.3 7.6 1.3 0.1 100.

0

75T75T75T75T75T75T75T75TType of Employment (Contractual/Non-contractual) by Education Level

Highest Level of Education Non-contractual Job

(%) Contractual Job

(%)

Did Not Finish Primary School 60.3 39.7

Primary Education 90.3 9.7

Lower Secondary Education 68.2 31.8

Upper Secondary – General 45.8 54.2

Upper Secondary – Vocational 43.5 56.5

Post Secondary – Vocational 12.7 87.3

Tertiary 11.9 88.1

Post Graduate 4.2 95.8

Doctorate 0.0 100.0

Total 40.7 59.3


75T75T75T75T75T75T75T75TType of Employment (Type of Employer) by Field of Study

Field of Study

Government, Public Sector

or Security Forces

(%)

State-Owned

Enterprise (%)

Private Company

or Enterprise

(%)

International Organization

(%)

Ngo, Non-Profit or

Humanitarian Organization

(%)

Self-Employed

(%)

Other Private

Individual (%)

Other (%)

General Programs 19.3 3.1 68.1 0.5 0.8 1.5 6.7 0.0

Teacher Training and Education Science

76.0 7.4 15.7 0.0 0.1 0.0 0.9 0.0

Humanities, Languages and Arts 48.9 4.2 41.7 1.8 0.8 0.1 2.5 0.0

Social Sciences 21.4 6.7 63.4 1.5 1.0 0.9 5.0 0.1

Business 28.6 2.7 60.9 1.2 5.2 0.0 1.4 0.0

Law 47.3 6.1 38.7 1.6 1.3 0.3 4.7 0.1

Life Science (Including Biology and Environmental Science)

67.2 6.3 22.7 0.3 0.3 0.0 2.0 1.1

Physical Science (Including Physics, Chemistry, and Earth Science)

27.3 4.4 60.8 0.5 1.2 0.8 4.8 0.3

Mathematics and Statistics 23.3 3.2 67.3 1.0 0.7 0.0 4.5 0.0

Computer Science 25.9 8.0 60.5 1.2 0.1 0.0 4.3 0.0

Engineering 21.3 12.1 59.4 1.5 0.5 1.8 3.3 0.0

Manufacturing and Construction 19.4 2.9 65.3 2.0 0.0 1.8 8.7 0.0

Agriculture and Veterinary 21.6 4.4 62.6 3.1 0.8 0.7 6.7 0.0

Health and Welfare 48.6 13.5 33.8 0.7 0.2 1.1 2.0 0.0

Crafts and Trades 17.7 7.0 68.3 0.5 0.4 0.8 5.4 0.0

Administrative and Clerical 37.6 7.6 54.2 0.5 0.0 0.0 0.0 0.0

Services 21.0 6.3 68.8 0.0 1.1 0.0 2.8 0.0

Total 29.4 7.0 56.6 1.1 0.7 0.8 4.3 0.1


75T75T75T75T75T75T75T75TType of Employment (Job Characteristics) by Field of Study

Field of Study Employee

(%)

Self-Employed with Employees

(%)

Self-Employed Without Employees

(%)


(%)

General Programs 55.1 5.5 14.7 24.7

Teacher Training and Education Science 88.7 0.6 5.3 5.4

Humanities, Languages and Arts 84.9 3.0 6.2 6.0

Social Sciences 71.6 5.8 10.6 12.1

Business 77.0 5.3 4.6 13.2

Law 79.2 2.5 6.1 12.2

Life Science (Including Biology and Environmental Science) 83.4 1.1 9.7 5.8

Physical Science (Including Physics, Chemistry, and Earth Science)

68.2 5.2 7.1 19.4

Mathematics and Statistics 71.4 5.1 11.5 12.2

Computer Science 76.6 4.6 10.0 8.9

Engineering 68.1 6.6 12.8 12.5

Manufacturing and Construction 65.6 11.4 10.4 12.7

Agriculture and Veterinary 59.3 12.5 14.8 13.5

Health and Welfare 87.6 2.3 2.5 7.7

Crafts and Trades 67.3 5.8 11.6 15.3

Administrative and Clerical 71.1 0.7 16.1 12.2

Services 67.4 1.0 15.2 16.4

Total 70.5 5.3 10.5 13.7


75T75T75T75T75T75T75T75TType of Employment (Contractual/Non-contractual) by Field of Study

Field of Study Non- Contractual Job

(%) Contractual Job

(%)

General Programs 47.2 52.9

Teacher Training and Education Science 6.5 93.5

Humanities, Languages and Arts 20.7 79.3

Social Sciences 36.4 63.6

Business 18.6 81.4

Law 24.3 75.7

Life Science (Including Biology and Environmental Science) 10.7 89.3

Physical Science (Including Physics, Chemistry, and Earth Science) 39.9 60.1

Mathematics and Statistics 34.7 65.3

Computer Science 31.1 68.9

Engineering 32.2 67.8

Manufacturing and Construction 42.4 57.6

Agriculture and Veterinary 44.0 56.0

Health and Welfare 14.3 85.7

Crafts and Trades 47.0 53.0

Administrative and Clerical 41.9 58.1

Services 51.7 48.3

Total 33.7 66.3


Vulnerable Employment by Age

Age Group Vulnerable Employment (%)

15-24 31.45

25-34 23.65

35-44 37.08

45-54 39.41

55-64 41.86

65-74 85.48

Vulnerable Employment by Education Level

Highest Level Of Education Vulnerable Employment (%)




Upper Secondary - General 34.31

Upper Secondary - Vocational 26.39

Post Secondary - Vocational 18.37

Tertiary 9.83

Post Graduate 4.78

Doctorate 1.56


Entrepreneurship

75T75T75T75T75T75T75T75TLevel of Entrepreneurship by Age

Age Group Already started a business

(%) Starting a business

(%)

15-24 3.6 2.1

25-34 6.1 3.2

35-44 9.0 2.3

45-54 6.0 3.3

55-64 4.9 1.5

65-74 3.6 2.0

75T75T75T75T75T75T75T75TLevel of Entrepreneurship by Region

Region Already started a business

(%) Starting a business

(%)

Ferizaj 7.8 1.1

Gjakova 6.0 1.0

Gjilan 8.3 3.6

Mitrovica 3.9 2.1

Peja 7.1 1.7

Pristina 4.5 1.5

Prizren 7.3 6.4

75T75T75T75T75T75T75T75TShare of Registered Businesses by Sex

Sex Registered Business

(%)

Male 52.5

Female 51.8

Total 52.3


75T75T75T75T75T75T75T75TShare of Registered Businesses by Region

Region Registered Business

(%)

Ferizaj 60.7

Gjakova 55.8

Gjilan 41.8

Mitrovica 40.2

Peja 52.1

Pristina 57.0

Prizren 51.5

Total 52.3

75T75T75T75T75T75T75T75TSources of Start-up Capital by Sex

Sex Own

Resources/Savings (%)

Formal Loan (%)

Informal Loan (%)

Remittances (%)

Other (%)

Male 61.1 18.7 3.2 9.7 7.3

Female 55.8 27.7 3.3 6.3 6.9

Total 60.1 20.4 3.2 9.1 7.3


75T75T75T75T75T75T75T75TSources of Start-up Capital by Ethnicity

Ethnicity

Own Resources/Savings

(%)

Formal Loan (%)

Informal Loan (%)

Remittances (%)

Other (%)

Albanian 60.4 20.6 2.6 9.8 6.6

Serbian 100.0 0.0 0.0 0.0 0.0

Montenegrin 0.0 0.0 0.0 0.0 0.0

Croat 0.0 0.0 0.0 0.0 0.0

Turkish 42.0 58.0 0.0 0.0 0.0

Bosnian 48.9 13.4 24.2 0.0 13.6

Roma 23.5 76.5 0.0 0.0 0.0

Gorani 57.7 0.0 0.0 0.0 42.3

Ashkali 87.2 0.0 12.8 0.0 0.0

Egyptian 48.9 0.0 0.0 51.1 0.0

Other 0.0 0.0 0.0 0.0 0.0

Total 60.1 20.4 3.2 9.1 7.3


75T75T75T75T75T75T75T75TMain Obstacles in Growing Business by Sex

Main Obstacle in Growing Business Male (%)

Female (%)

Total (%)

Lack of Financial Means 65.4 63.2 65.0

Lack of Market and Clients 13.4 13.5 13.4

High Competition 10.0 9.4 9.9

Lack of Suitable Workplace 2.2 3.9 2.4

Institutional Barriers and Lack of Institutional Support 3.5 3.5 3.5

Lack of Working Tools and Professionalism 3.1 3.5 3.2

High Interest Rates 1.3 1.3 1.3

Unfavorable Climate Conditions 0.3 1.7 0.5

Personal Reasons 1.0 0.1 0.9

Total Sample 56.4 43.6 100.0

75T75T75T75T75T75T75T75TMain Obstacles in growing Business by Region

Main Obstacle in Growing Business Ferizaj

(%) Gjakova

(%) Gjilan

(%) Mitrovica

(%) Peja (%)

Pristina (%)

Prizren (%)

Total (%)

Lack of Financial Means 76.0 61.6 57.5 57.2 58.6 67.1 69.0 65.0

Lack of Market and Clients 11.3 12.9 11.3 20.0 9.3 3.3 17.1 13.4

High Competition 3.0 12.3 14.0 16.6 14.4 10.0 6.2 9.9

Lack of Suitable Workplace 1.3 1.5 4.6 2.4 1.8 4.8 1.8 2.4

Institutional Barriers and Lack of Institutional Support 3.1 7.2 10.7 2.4 3.7 5.1 0.0 3.5

Lack of Working Tools and Professionalism 1.7 0.0 0.0 1.4 6.8 4.0 4.8 3.2

High Interest Rates 3.6 3.5 0.0 0.0 0.0 1.2 1.1 1.3

Unfavorable Climate Conditions 0.0 0.5 2.0 0.0 3.0 0.0 0.0 0.5

Personal Reasons 0.0 0.6 0.0 0.0 2.5 4.5 0.0 0.9


75T75T75T75T75T75T75T75TEntrepreneurship as a Desirable Career Choice by Sex

Sex Is Entrepreneurship Considered a Desirable Choice in Kosovo?

(% that responded “Yes”)

Male 96.5

Female 98.7

Total 97.6

75T75T75T75T75T75T75T75TIf Entrepreneurship is Not Considered a Desirable Career Choice, Why Not by Sex

Reason entrepreneurship is not considered a desirable choice

Male (%)

Female (%)

Total (%)

Corruption and Unsafe Environment 33.3 8.4 27.2

Unfavorable Conditions for Doing Business 15.0 9.5 13.6

Lack of Financial Means 4.3 8.2 5.3

High Competition 4.4 7.8 5.3

No Profit, Lack of Market 12.9 15.2 13.5

High Taxes 1.1 1.0 1.1

Weak Economy and Lack of Institutional Support 14.1 31.2 18.2

Fear of Failure 5.0 4.2 4.8

Lack of Perspective 8.2 14.7 9.8

Lack of Qualification and Professionalism 1.7 0.0 1.3


75T75T75T75T75T75T75T75TEntrepreneurship as a Desirable Career Choice by Ethnicity

Ethnicity Is Entrepreneurship Considered a Desirable Choice?

(% that responded “Yes”)

Albanian 97.8

Serbian 89.1

Montenegrin 100.0

Croat 100.0

Turkish 91.7

Bosnian 97.8

Roma 92.9

Gorani 91.0

Ashkali 99.2

Egyptian 98.5

Other 100.0

Total 97.6


75T75T75T75T75T75T75T75TIf Entrepreneurship is Not Considered a Desirable Career Choice, Why Not by Ethnicity

Albanian (%)

Serbian (%)

Montenegrin (%)

Croat (%)

Turkish (%)

Bosnian (%)

Roma (%)

Gorani (%)

Ashkali (%)

Egyptian (%)

Other (%)

Total (%)

Corruption and Unsafe Environment

27.7 27.1 0.0 0.0 0.0 75.7 17.5 0.0 100.0 37.8 0.0 27.2

Unfavorable Conditions for Doing Business

11.5 0.0 0.0 0.0 30.3 0.0 0.0 100.0 0.0 0.0 0.0 13.6

Lack of Financial Means 4.2 27.2 0.0 0.0 0.0 9.7 30.2 0.0 0.0 0.0 0.0 5.3

High Competition 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 62.2 0.0 5.3

No Profit, Lack of Market 14.3 23.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13.5

High Taxes 1.0 0.0 0.0 0.0 11.1 0.0 0.0 0.0 0.0 0.0 0.0 1.1

Weak Economy and Lack of Institutional Support

18.0 10.6 0.0 0.0 58.6 14.6 52.4 0.0 0.0 0.0 0.0 18.2

Fear of Failure 5.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.8

Lack of Perspective 10.6 11.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.8

Lack of Qualification and Professionalism

1.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.3


Time Use

75T75T75T75T75T75T75T75TTime Use, Previous day: Detailed Activities

Time Spent on Activities (Hours)

Sex Geographic

Classification Unemployment

Labor force Participation

Activity (Detailed) Male Female Urban Rural Employed Unemployed Inactive Active Total

Sleep 8.1 8.3 8.2 8.2 7.8 8.6 8.5 7.9 8.2

Sick in Bed 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Eating 1.9 2.2 1.9 2.1 1.8 2.2 2.2 1.8 2.0

Washing and Dressing 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

Other or Unspecified Personal Care 0.2 0.2 0.1 0.2 0.2 0.1 0.1 0.2 0.2

Working Time (including Coffee Breaks and Travel at Work)

3.4 0.7 2.2 1.7 4.5 0.0 0.0 3.8 1.9

Activities Related to Employment - Lunch Break

0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.1 0.1

Activities Related to Employment - Job Search 0.1 0.0 0.0 0.0 0.0 0.3 0.0 0.1 0.0

Activities Related to Employment - Other or Unspecified

0.1 0.0 0.1 0.1 0.1 0.0 0.0 0.1 0.1

Unspecified Study 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

School or University - Classes and Lectures 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

School or University - Homework 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Free Time Study 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Food Preparation, Baking and Preserving 0.0 2.2 1.1 1.3 0.8 0.9 1.7 0.8 1.3

Dish Washing 0.0 0.6 0.3 0.4 0.2 0.3 0.5 0.2 0.4

Childcare - Physical Care and Supervision 0.1 0.6 0.4 0.4 0.2 0.4 0.5 0.2 0.4

Childcare - Teaching the Child 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0

Childcare - Reading, Playing and Talking with Child

0.0 0.0 0.1 0.0 0.0 0.1 0.1 0.0 0.0

Childcare - Accompanying Child 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0

Childcare - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Help to Adult Family Member - Physical Care of Dependent Adult HH Member

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Sex Geographic




Help to Adult Family Member - Other Help of Dependent Adult HH Member

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Help to Adult Family Member - Help to Non-Dependent Adult HH Member

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Household Upkeep - Cleaning Dwelling 0.1 2.2 1.2 1.3 0.7 1.2 1.7 0.8 1.3

Household Upkeep - Cleaning Garden 0.5 0.3 0.2 0.4 0.3 0.4 0.4 0.3 0.4

Household Upkeep - Heating and Water 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Household Upkeep - Arranging Household Goods and Materials

0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Household Upkeep - Other or Unspecified 0.1 0.0 0.0 0.1 0.0 0.2 0.1 0.1 0.1

Making and Care for Textiles - Laundry 0.0 0.1 0.1 0.1 0.0 0.1 0.1 0.0 0.1

Making and Care for Textiles - Ironing 0.0 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Making and Care for Textiles - Handicraft and Producing Textiles

0.0 0.1 0.1 0.1 0.0 0.1 0.1 0.0 0.1

Making and Care for Textiles - Other or Unspecified

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Gardening and Pet Care - Gardening 0.8 0.4 0.3 0.7 0.5 0.5 0.6 0.5 0.6

Gardening and Pet Care - Tending Domestic Animals

0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0

Gardening and Pet Care - Caring for Pets 0.3 0.1 0.0 0.3 0.3 0.1 0.2 0.3 0.2

Gardening and Pet Care - Walking the Dog 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Gardening and Pet Care - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Construction and Repairs - House Construction and Renovation

0.1 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0

Construction and Repairs - Repairs to Dwelling 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0

Construction and Repairs - Making, Repairing and Maintaining Equipment

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Construction and Repairs - Vehicle Maintenance

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Construction and Repairs - Other or Unspecified

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Shopping and Services - Shopping 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1



Sex Geographic




Shopping and Services - Commercial and Administrative Services

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Shopping and Services - Personal Services 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Shopping and Services - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Household Management 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Unspecified Household and Family Care 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

organizational Work (Work for or Through An organization)

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

informal Help to Other Households - Construction and Repairs As Help

0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0

informal Help to Other Households - Help in Employment and Farming

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

informal Help to Other Households - Care of Own Children Living in Another Hh

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

informal Help to Other Households - Other Childcare As Help to Another Hh

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

informal Help to Other Households - Help to An Adult of Another Hh

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

informal Help to Other Households - Other or Unspecified

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Participatory Activities - Meetings 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Participatory Activities - Religious Activities 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1

Participatory Activities - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Social Life - Socializing with Family 0.3 0.3 0.3 0.3 0.2 0.4 0.3 0.3 0.3

Social Life - Visiting and Receiving Visitors 0.7 0.8 0.8 0.7 0.6 1.0 0.9 0.6 0.8

Social Life - Celebrations 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Social Life - Telephone Conversation 0.2 0.1 0.2 0.1 0.1 0.3 0.2 0.1 0.2

Social Life - Other or Unspecified 0.1 0.0 0.1 0.0 0.0 0.1 0.1 0.0 0.1

Entertainment and Culture - Cinema 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Entertainment and Culture - theater and Concerts

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Sex Geographic




Entertainment and Culture - Art Exhibitions and Museums

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Entertainment and Culture - Library 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Entertainment and Culture - Sports Events 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Entertainment and Culture - Other or Unspecified

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Resting - Time Out 2.1 1.7 1.8 1.9 1.7 2.0 2.0 1.7 1.9

Physical Exercise - Walking and Hiking 0.5 0.2 0.4 0.2 0.2 0.4 0.4 0.3 0.3

Physical Exercise - Jogging and Running 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Physical Exercise - Cycling, Skiing and Skating 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Physical Exercise - Ball Games 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Physical Exercise - Gymnastics and Fitness 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Physical Exercise - Water Sports 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Physical Exercise - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Productive Exercise (Hunting, Fishing, Picking Berries, Mushrooms, Herbs)

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Sports Related Activities 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Arts and Hobbies - Arts (Visual, Performing, Literary)

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Arts and Hobbies - Collecting 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Arts and Hobbies - Correspondence 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Arts and Hobbies - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Computing - Programming 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Computing - information By Computing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Computing - Communication by Computing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Computing - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Games - Solo Games and Playing, Gambling 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Games - Parlor Games and Play 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Games - Computer Games 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Sex Geographic




Games - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Reading - Periodicals 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Reading - Books 0.1 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.1

Reading - Other or Unspecified 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Watching Tv, Video or Dvd 1.9 1.1 1.6 1.4 1.3 1.7 1.6 1.4 1.5

Listening to Radio or Recordings 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Travel - to/From Work 0.2 0.1 0.2 0.1 0.3 0.0 0.0 0.3 0.1

Travel - Related to Study 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Travel - Related to Shopping and Services 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0

Travel - Related to Childcare 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Travel - Related to Other Household Care 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Travel - Related to Voluntary Work and Meetings

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Travel - Related to Social Life 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1

Travel - Related to Other Leisure 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Travel - Related to Changing Locality 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Travel - Other or Unspecified 0.1 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.1

Auxiliary Codes - Unspecified Leisure Time 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0

(mean) Auxiliary Codes - Other 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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ARLINGTON, VA 22201

mcc kosovo labor force and time use study research report · 2020. 1. 23. · mcc kosovo lftus 2017...

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